Skocz do pozycji: 1.

Tytuł:

Passivity-based optimal control of discrete-time nonlinear systems

Autorzy:

Binazadeh, T.
Shafiei, M. H.

Powiązania:

https://bibliotekanauki.pl/articles/205917.pdf

Data publikacji:

2013

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

nonlinear discrete-time systems optimal passivity-based control
genetic optimization algorithm

Opis:

In this paper, a passivity-based optimal controlmethod for a broad class of nonlinear discrete-time systems is proposed. The resulting control law is a static output feedback law which is practically preferred with respect to the state feedback law and is simple to implement. The control law has a general structure with adjustable parameters which are tuned, using an optimization method (genetic algorithm), to minimize an arbitrary cost function. By choosing this cost function it is possible to shape the transient response of the closed-loop system, as it is desirable. An illustrative ex ample shows the effectiveness of the proposed approach.

Źródło:

Control and Cybernetics; 2013, 42, 3; 627-637
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 2.

Tytuł:

A Comparative Study of PID Controller Tuning Using GA, EP, PSO and ACO

Autorzy:

Nagaraj, B.
Vijayakumar, P.

Powiązania:

https://bibliotekanauki.pl/articles/384767.pdf

Data publikacji:

2011

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

ant colony algorithm
evolutionary program
genetic algorithm particle swarm optimization and soft computing

Opis:

Proportional - Integral - Derivative control schemes continue to provide the simplest and effective solutions to most of the control engineering applications today. How ever PID controller are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. This article comes up with a hybrid approach involving Genetic Algorithm (GA), Evolutionary Pro gramming (EP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The proposed hybrid algorithm is used to tune the PID parameters and its per formance has been compared with the conventional me thods like Ziegler Nichols and Cohen Coon method. The results obtained reflect that use of heuristic algorithm based controller improves the performance of process in terms of time domain specifications, set point tracking, and regulatory changes and also provides an optimum stability. Speed control of DC motor process is used to assess the efficacy of the heuristic algorithm methodology

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 2; 42-48
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

Tytuł:

Some Aspects of the Application of Genetic Algorithm for Solving the Assignment Problem of Tasks to Resources in a Transport Company

Autorzy:

Izdebski, Mariusz
Jacyna, Marianna

Powiązania:

https://bibliotekanauki.pl/articles/504281.pdf

Data publikacji:

2014

Wydawca:

Międzynarodowa Wyższa Szkoła Logistyki i Transportu

Tematy:

assignment problem
genetic algorithm
optimization

Opis:

The article defines the assignment problem of tasks to resources in a transport company. The paper describes mathematical model of a transport system taking into account the assignment of vehicles to the tasks. It also provides stages of creation of the genetic algorithm for solving the assignment problem in the transport company.

Źródło:

Logistics and Transport; 2014, 21, 1; 13-20
1734-2015

Pojawia się w:

Logistics and Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 4.

Tytuł:

Optimization of the pulse transformer using circuit-field model

Autorzy:

Łyskawiński, W.
Knypiński, Ł.
Nowak, L.
Jędryczka, C.

Powiązania:

https://bibliotekanauki.pl/articles/1395761.pdf

Data publikacji:

2013

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

pulse transformer
optimization
genetic algorithm

Opis:

The paper presents the new strategy of the optimization of pulse transformer (PT). In order to reduce calculation time the optimization problem has been decomposed into two stages. In the first stage, to determine functional parameters of PT the circuit model is used. The goal of circuit calculations is to limit the space of design variables that meets formulated requirements. The genetic algorithm has been applied for this task. In order to include constraints, the penalty function has been engaged. The transformer dimensions obtained in the first stage of calculations are used as initial values in the second stage of design process. In the second stage the field model of PT is employed. Obtained results prove that presented approach allows for fast optimization of the PT design.

Źródło:

Computer Applications in Electrical Engineering; 2013, 11; 159-167
1508-4248

Pojawia się w:

Computer Applications in Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 5.

Tytuł:

On Some Aspects of Genetic and Evolutionary Methods for Optimization Purposes

Autorzy:

Woźniak, M.
Połap, D.

Powiązania:

https://bibliotekanauki.pl/articles/227353.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

computational intelligence
genetic algorithm
heuristic algorithm
optimization

Opis:

In this paper, the idea of applying some hybrid genetic algorithms with gradient local search and evolutionary optimization techniques is formulated. For two different test functions the proposed versions of the algorithms have been examined. Research results are presented and discussed to show potential efficiency in optimization purposes.

Źródło:

International Journal of Electronics and Telecommunications; 2015, 61, 1; 7-16
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 6.

Tytuł:

Multi-variable optimization of an ytterbium-doped fiber laser using genetic algorithm

Autorzy:

Hashemi, S. S.
Ghavami, S. S.
Khorsandi, A

Powiązania:

https://bibliotekanauki.pl/articles/175086.pdf

Data publikacji:

2015

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

fiber laser
optimization
genetic algorithm

Opis:

We introduce the genetic algorithm for the optimization of an Yb3+-doped double-clad fiber laser based on a multi-variable scheme. The output characteristic of the laser is numerically simulated using real practical values. This is performed through solving the associated steady-state rate equation and investigating the effects of input variables such as pump and signal wavelengths and length of the fiber on the laser output. It is found that pumping of the medium around 975 nm is conducted to attain the maximum output power of ~34.8 W, while the stability of the outcoupled power is significantly improved when pumping at 920 nm, confirming good agreement with the reported experimental results. We have also found that by using genetic algorithm base multi-variable optimization, the output power can be significantly increased by about three orders of magnitude and reaches to ~28.5 W with optimum and shorter fiber length of ~57.5 m. Obtained results show that based on the genetic algorithm multi-variable discipline, fiber characteristics can be optimized according to the gaining of maximum output power.

Źródło:

Optica Applicata; 2015, 45, 3; 355-367
0078-5466
1899-7015

Pojawia się w:

Optica Applicata

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 7.

Tytuł:

Structural weight minimization of high speed vehicle-passenger catamaran by genetic algorithm

Autorzy:

Sekulski, Z.

Powiązania:

https://bibliotekanauki.pl/articles/258678.pdf

Data publikacji:

2009

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

ship structure
optimization
topology optimization
sizing optimization
genetic algorithm

Opis:

Reduction of hull structural weight is the most important aim in the design of many ship types. But the ability of designers to produce optimal designs of ship structures is severely limited by the calculation techniques available for this task. Complete definition of the optimal structural design requires formulation of size-topology-shape-material optimization task unifying optimization problems from four areas and effective solution of the problem. So far a significant progress towards solution of this problem has not been achieved. In other hand in recent years attempts have been made to apply genetic algorithm (GA) optimization techniques to design of ship structures. An objective of the paper was to create a computer code and investigate a possibility of simultaneous optimization of both topology and scantlings of structural elements of large spacial sections of ships using GA. In the paper GA is applied to solve the problem of structural weight minimisation of a high speed vehicle-passenger catamaran with several design variables as dimensions of the plate thickness, longitudinal stiffeners and transverse frames and spacing between longitudinals and transversal members. Results of numerical experiments obtained using the code are presented. They shows that GA can be an efficient optimization tool for simultaneous design of topology and sizing high speed craft structures.

Źródło:

Polish Maritime Research; 2009, 2; 11-23
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 8.

Tytuł:

Optimization of dental implant using genetic algorithm
Optymalizacja wszczepu stomatologicznego z wykorzystaniem algorytmu genetycznego

Autorzy:

Łodygowski, T.
Szajek, K.
Wierszycki, M.

Powiązania:

https://bibliotekanauki.pl/articles/279418.pdf

Data publikacji:

2009

Wydawca:

Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej

Tematy:

design optimization
genetic algorithm
dental implant

Opis:

The subject of the present work is optimization of the modern implant system Osteoplant, which was created and is still developed by Foundation of University of Medical Sciences in Poznań. Clinical observations point to the occurrence of both early and late complications in the case of all two-component implant systems. In many cases, these problems are caused by mechanical fractures of the implants themselves. The obtained results of the previous studies focused on necessary changes of the implant mechanical behavior, which helped to achieve the required long-term strength. However, modifications of the present dental implant system are not obvious. In this paper, an optimization of the Osteoplant dental implant system, with the use of FEA and genetic algorithms is discussed.
Przedmiotem prezentowanej pracy jest problem optymalizacji systemu implantologicznego Osteoplant, który został opracowany i wciąż jest ulepszany przez Fundację Uniwersytetu Medycznego w Poznaniu. Obserwacje kliniczne potwierdzają występowanie powikłań zarówno we wczesnej, jak i późnej fazie użytkowania implantu. Dotychczas otrzymane wyniki wskazują, że wydłużenie bezawaryjnego okresu użytkowania implantu wymaga wprowadzenia zmian w jego pracy mechanicznej. Jednakże, ustalenie szczegłów modyfikacji nie jest oczywiste. W artykule została opisana procedura optymalizacji systemu implantologicznego Osteoplant z użyciem analizy metodą elementów skończonych oraz algorytmu genetycznego.

Źródło:

Journal of Theoretical and Applied Mechanics; 2009, 47, 3; 573-598
1429-2955

Pojawia się w:

Journal of Theoretical and Applied Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 9.

Tytuł:

Efficiency improvement of switched reluctance generator using optimization techniques

Autorzy:

Reis, M. R. C.
Araújo, W. R. H.
Calixto, W. P.

Powiązania:

https://bibliotekanauki.pl/articles/136169.pdf

Data publikacji:

2017

Wydawca:

EEEIC International Barbara Leonowicz Szabłowska

Tematy:

switched reluctance generator
optimization
control
genetic algorithm

Opis:

This article introduces the switched reluctance machine operating as a generator. This kind of electrical machine delivers CC power at the output and the energy generated can be controlled through several variables. In this work, the switching angles of the machine's power converter are optimized using deterministic and heuristic techniques so that the output power is kept constant via PI controller while guaranteeing maximum value for machine performance, even for different excitation values and mechanical power on the shaft.

Źródło:

Transactions on Environment and Electrical Engineering; 2017, 2, 1; 74-80
2450-5730

Pojawia się w:

Transactions on Environment and Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

Tytuł:

Ship Trajectory Control Optimization in Anti-collision Maneuvering

Autorzy:

Zhang, J. F.
Yang, X. D.
Zhang, D.
Haugen, S.

Powiązania:

https://bibliotekanauki.pl/articles/116373.pdf

Data publikacji:

2013

Wydawca:

Uniwersytet Morski w Gdyni. Wydział Nawigacyjny

Tematy:

anticollision
ship trajectory
genetic algorithm
route optimization

Opis:

A lot of attention is being paid to ship’s intelligent anti‐collision by researchers. Several solutions have been introduced to find an optimum trajectory for ship, such as Game Theory, Genetic or Evolutionary Algorithms and so on. However, ship’s maneuverability should be taken into consideration before their real applications. Ship’s trajectory control in anti‐collision maneuvering is studied in this paper. At first, a simple linear ship maneuverability model is introduced to simulate its movement under different speed and rudder angle. After that, ship’s trajectory control is studied by considering the duration of rudder, operation distance to turning points, and maximum angular velocity. The details for algorithm design are also introduced. By giving some restrictions according to the requirements from COLREGs, the intervals for rudder angle in different circumstances can be determined based on the curves. The results can give very meaningful guidance for seafarers when making decisions.

Źródło:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2013, 7, 1; 89-93
2083-6473
2083-6481

Pojawia się w:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 11.

Tytuł:

Passive loop coordinates optimization for mitigation of magnetic field value in the proximity of a power line

Autorzy:

Książkiewicz, M.

Powiązania:

https://bibliotekanauki.pl/articles/97704.pdf

Data publikacji:

2015

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

power line
magnetic field
optimization
genetic algorithm

Opis:

The paper relates to the distribution of the magnetic field generated by the overhead power line, and it’s reduction in the area of interest using a conductive loop placed in the space near the line. The paper presents results obtained from an original program written in C ++, which implements the procedure for calculating the magnetic field generated by overhead line and a genetic algorithm used to optimize the location and loop compensation factor. Examples of the program are presented for horizontal single-track line and three different shielding loop configurations. The first relates to a single loop (4 to 5 parameters to optimize - 4 position coordinates (y, z) and the compensation factor), the second case involves two loops with one common conductor (6 to 8 parameters - 6 coordinates (y, z) and 0 to 2 compensation factors), the third case concerns two independent loops (8 to 10 parameters - 8 coordinates (y, z) and 0 to 2 of the compensation factors). In addition similar calculations are performed for single-track line with two earth wires.

Źródło:

Computer Applications in Electrical Engineering; 2015, 13; 77-87
1508-4248

Pojawia się w:

Computer Applications in Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

Tytuł:

Optimizing the number of docks at transhipment terminals using genetic algorithm

Autorzy:

Izdebski, M.
Jacyna-Gołda, I.

Powiązania:

https://bibliotekanauki.pl/articles/242009.pdf

Data publikacji:

2017

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

transhipment terminal
genetic algorithm
optimization
cross docking

Opis:

This article presents the issue of designating the number of docks at the transhipment terminals using genetic algorithm. Transhipment terminals refer to cross-docking terminals. The main factor that influences on the number of these docks is the stream of cargo flowing into the given terminal. In order to determine this flow of cargo the mathematical model of the distribution of this flow was developed. This model takes into account constraints like those that e.g. processing capacity at the transhipment terminal cannot be exceeded or demand of recipients must be met. The criterion function in this model determines the minimum cost of the flow of cargo between all objects in the transport network. To designate the optimal stream of cargo flowing into the transport network the genetic algorithm was developed. In this article, the stages of construction of this algorithm were presented. The structure processed by the algorithm, the process of crossover and mutation were described. In the article in order to solve the problem of designating the number of docks at the transhipment terminals the genetic algorithm was developed.

Źródło:

Journal of KONES; 2017, 24, 4; 369-376
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 13.

Tytuł:

An Assessment of High-Order-Mode Analysis and Shape Optimization of Expansion Chamber Mufflers

Autorzy:

Chiu, M. C.
Chang, Y. C.

Powiązania:

https://bibliotekanauki.pl/articles/178079.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

higher order wave
eigenfunction
optimization
genetic algorithm

Opis:

A substantial quantity of research on muffler design has been restricted to a low frequency range using the plane wave theory. Based on this theory, which is a one-dimensional wave, no higher order wave has been considered. This has resulted in underestimating acoustical performances at higher frequencies when doing muffler analysis via the plane wave model. To overcome the above drawbacks, researchers have assessed a three-dimensional wave propagating for a simple expansion chamber muffler. Therefore, the acoustic effect of a higher order wave (a high frequency wave) is considered here. Unfortunately, there has been scant research on expansion chamber mufflers equipped with baffle plates that enhance noise elimination using a higher-order-mode analysis. Also, space-constrained conditions of industrial muffler designs have never been properly addressed. So, in order to improve the acoustical performance of an expansion chamber muffler within a constrained space, the optimization of an expansion chamber muffler hybridized with multiple baffle plates will be assessed. In this paper, the acoustical model of the expansion chamber muffler will be established by assuming that it is a rigid rectangular tube driven by a piston along the tube wall. Using an eigenfunction (higher- order-mode analysis), a four-pole system matrix for evaluating acoustic performance (STL) is derived. To improve the acoustic performance of the expansion chamber muffler, three kinds of expansion chamber mufflers (KA-KC) with different acoustic mechanisms are introduced and optimized for a targeted tone using a genetic algorithm (GA). Before the optimization process is performed, the higher-order-mode mathematical models of three expansion chamber mufflers (A-C) with various allocations of inlets/outlets and various chambers are also confirmed for accuracy. Results reveal that the STL of the expansion chamber mufflers at the targeted tone has been largely improved and the acoustic performance of a reverse expansion chamber muffler is more efficient than that of a straight expansion chamber muffler. Moreover, the STL of the expansion chamber mufflers will increase as the number of the chambers that separate with baffles increases.

Źródło:

Archives of Acoustics; 2014, 39, 4; 489-499
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 14.

Tytuł:

Performance investigation and element optimization of 2D array transducer using Bat Algorithm

Autorzy:

Tantawy, Dina Mohamed
Eladawy, Mohamed
Hassan, Mohamed Alimaher
Mubarak, Roaa

Powiązania:

https://bibliotekanauki.pl/articles/140685.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

2D ultrasound arrays
Binary Bat Algorithm
Genetic Algorithm
Optimization

Opis:

One of the least expensive and safest diagnostic modalities routinely used is ultrasound imaging. An attractive development in this field is a two-dimensional (2D) matrix probe with three-dimensional (3D) imaging. The main problems to implement this probe come from a large number of elements they need to use. When the number of elements is reduced the side lobes arising from the transducer change along with the grating lobes that are linked to the periodic disposition of the elements. The grating lobes are reduced by placing the elements without any consideration of the grid. In this study, the Binary Bat Algorithm (BBA) is used to optimize the number of active elements in order to lower the side lobe level. The results are compared to other optimization methods to validate the proposed algorithm.

Źródło:

Archives of Electrical Engineering; 2020, 69, 3; 561-579
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 15.

Tytuł:

Planning and management of aircraft maintenance using a genetic algorithm

Autorzy:

Kowalski, Mirosław
Izdebski, Mariusz
Żak, Jolanta
Gołda, Paweł
Manerowski, Jerzy

Powiązania:

https://bibliotekanauki.pl/articles/1841824.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

aircraft operation
maintenance
multi-criteria optimization
genetic algorithm

Opis:

The aim of the article was to develop a tool to support the process of planning and managing aircraft (ac) maintenance. Aircraft maintenance management has been presented for scheduled technical inspections resulting from manufacturers’ technical documentation for ac. The authors defined the problem under investigation in the form of a four-phase decisionmaking process taking into account assignment of aircraft to airports and maintenance stations, assignment of crew to maintenance points, setting the schedules, i.e. working days on which aircraft are directed to maintenance facilities. This approach to the planning and management of aircraft maintenance is a new approach, unprecedented in the literature. The authors have developed a mathematical model for aircraft maintenance planning and management in a multi-criteria approach and an optimisation tool based on the operation of a genetic algorithm. To solve the problem, a genetic algorithm was proposed. The individual steps of the algorithm construction were discussed and its effectiveness was verified using real data.

Źródło:

Eksploatacja i Niezawodność; 2021, 23, 1; 143-153
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 16.

Tytuł:

Recognizing Sets in Evolutionary Multiobjective Optimization

Autorzy:

Gajda-Zagórska, E.

Powiązania:

https://bibliotekanauki.pl/articles/308467.pdf

Data publikacji:

2012

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

basin of attraction
clustering
genetic algorithm
multiobjective optimization

Opis:

Among Evolutionary Multiobjective Optimization Algorithms (EMOA) there are many which find only Paretooptimal solutions. These may not be enough in case of multimodal problems and non-connected Pareto fronts, where more information about the shape of the landscape is required. We propose a Multiobjective Clustered Evolutionary Strategy (MCES) which combines a hierarchic genetic algorithm consisting of multiple populations with EMOA rank selection. In the next stage, the genetic sample is clustered to recognize regions with high density of individuals. These regions are occupied by solutions from the neighborhood of the Pareto set. We discuss genetic algorithms with heuristic and the concept of well-tuning which allows for theoretical verification of the presented strategy. Numerical results begin with one example of clustering in a single-objective benchmark problem. Afterwards, we give an illustration of the EMOA rank selection in a simple two-criteria minimization problem and provide results of the simulation of MCES for multimodal, multi-connected example. The strategy copes with multimodal problems without losing local solutions and gives better insight into the shape of the evolutionary landscape. What is more, the stability of solutions in MCES may be analyzed analytically.

Źródło:

Journal of Telecommunications and Information Technology; 2012, 1; 74-82
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 17.

Tytuł:

Use of computer assistance in order to designate the tasks in the municipal services companies

Autorzy:

Izdebski, M.
Jacyna, M.

Powiązania:

https://bibliotekanauki.pl/articles/241863.pdf

Data publikacji:

2014

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

municipal services companies
transport
optimization
genetic algorithm
verification

Opis:

In this article, the method of designating the tasks in the municipal services companies was described. Presented method consists of three phase: the preparatory phase, the optimization phase and the generated tasks phase. Each phase was characterized. In this paper, the mathematical model of this problem was presented. The function of criterion and the condition on designating the tasks were defined. The minimum route described in the optimization phase was designated by the genetic algorithm. In this paper, the stages of constructing of the genetic algorithm were presented. A structure of the data processed by the algorithm, a function of adaptation, a selection of chromosomes, a crossover, a mutation and an inversion were characterized. A structure of the data was presented as string of natural numbers. In selection process, the roulette method was used and in the crossover, process the operator PMX was presented. The method was verified in programming language C #. The process of verification was divided into two stages. In the first stage, the best parameters of the genetics algorithm were designated. In the second stage, the algorithm was started with these parameters and the result was compared with the random search algorithm. The random search algorithm generates 2000 routes and the best result is compared with the genetic algorithm. The influence of the inversion, the mutation and the crossover on quality of the results was examined.

Źródło:

Journal of KONES; 2014, 21, 2; 105-112
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 18.

Tytuł:

Planning and management of aircraft maintenance using a genetic algorithm

Autorzy:

Kowalski, Mirosław
Izdebski, Mariusz
Żak, Jolanta
Gołda, Paweł
Manerowski, Jerzy

Powiązania:

https://bibliotekanauki.pl/articles/1841765.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

aircraft operation
maintenance
multi-criteria optimization
genetic algorithm

Opis:

The aim of the article was to develop a tool to support the process of planning and managing aircraft (ac) maintenance. Aircraft maintenance management has been presented for scheduled technical inspections resulting from manufacturers’ technical documentation for ac. The authors defined the problem under investigation in the form of a four-phase decisionmaking process taking into account assignment of aircraft to airports and maintenance stations, assignment of crew to maintenance points, setting the schedules, i.e. working days on which aircraft are directed to maintenance facilities. This approach to the planning and management of aircraft maintenance is a new approach, unprecedented in the literature. The authors have developed a mathematical model for aircraft maintenance planning and management in a multi-criteria approach and an optimisation tool based on the operation of a genetic algorithm. To solve the problem, a genetic algorithm was proposed. The individual steps of the algorithm construction were discussed and its effectiveness was verified using real data.

Źródło:

Eksploatacja i Niezawodność; 2021, 23, 1; 143-153
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 19.

Tytuł:

Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm. Part II. Computational simulations

Autorzy:

Sekulski, Z.

Powiązania:

https://bibliotekanauki.pl/articles/260598.pdf

Data publikacji:

2011

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

ship structure
multi-objective optimization
evolutionary algorithm
genetic algorithm
Pareto domination

Opis:

Real ship structural design problems are usually characterized by presence of many conflicting objectives. Simultaneously, a complete definition of the optimum structural design requires a formulation of size-topology-shape-material optimization task unifying the optimization problems of the four areas and giving an effective solution of the problem. So far, a significant progress towards the solution of the problem has not been obtained. An objective of the present paper was to develop an evolutionary algorithm for multiobjective optimization of structural elements of large spatial sections of ships. Selected elements of the multi-criteria optimization theory have been presented in detail. Methods for solution of the multi-criteria optimization problems have been discussed with the focus on the evolutionary optimization algorithms. In the paper an evolutionary algorithm where selection takes place based on the aggregated objective function combined with domination attributes as well as distance to the asymptotic solution, is proposed and applied to solve the problem of optimizing structural elements with respect to their weight and surface area on a high speed vehicle-passenger catamaran structure, with several design variables, such as plate thickness, scantlings of longitudinal stiffeners and transverse frames, and spacing between longitudinal and transversal members, taken into account. Details of the computational models were at the level typical for conceptual design. Scantlings were analyzed by using selected rules of a classification society. The results of numerical experiments with the use of the developed algorithm, are presented. They show that the proposed genetic algorithm can be an efficient tool for multi-objective optimization of ship structures. The paper is published in three parts: Part I: Theoretical background on evolutionary multi-objective optimization, Part II: Computational investigations, and Part III: Analysis of the results.

Źródło:

Polish Maritime Research; 2011, 3; 3-30
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 20.

Tytuł:

Function optimization using metaheuristics

Autorzy:

Pilski, M.
Seredyński, F.

Powiązania:

https://bibliotekanauki.pl/articles/92887.pdf

Data publikacji:

2006

Wydawca:

Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach

Tematy:

particle swarm optimization (PSO)
artificial immune system
genetic algorithm
function optimization

Opis:

The paper presents the results of comparison of three metaheuristics that currently exist in the problem of function optimization. The first algorithm is Particle Swarm Optimization (PSO) - the algorithm has recently emerged. The next one is based on a paradigm of Artificial Immune System (AIS). Both algorithms are compared with Genetic Algorithm (GA). The algorithms are applied to optimize a set of functions well known in the area of evolutionary computation. Experimental results show that it is difficult to unambiguously select one best algorithm which outperforms other tested metaheuristics.

Źródło:

Studia Informatica : systems and information technology; 2006, 1(7); 77-91
1731-2264

Pojawia się w:

Studia Informatica : systems and information technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 21.

Tytuł:

Analog Circuit Based on Computational Intelligence Techniques

Autorzy:

Oltean, G.
Hintea, S.
Şipos, E.

Powiązania:

https://bibliotekanauki.pl/articles/385049.pdf

Data publikacji:

2009

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

analog circuit design
optimization
genetic algorithm
neuro-fuzzy systems

Opis:

This paper presents a new method for analog circuit design optimization. Our approach turns to good account the advantages offered by computational intelligence techniques. Design objectives can be expressed in a flexible manner using fuzzy sets. This way appears the possibility to consider different degrees for requirement achievements and acceptability degree for a particular solution. Neuro-fuzzy systems (universal approximators) are used to model the complex multi-variable and nonlinear circuit performances. These models satisfy two main requirements: high accuracy and low computation complexity. An efficient and robust genetic algorithm does avoiding local minima the exploration of the large, multidimensional solution space in quest for the optimal solution.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2009, 3, 2; 63-69
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

Tytuł:

The application of genetic algorithm in the assignment problems in the transportation company

Autorzy:

Izdebski, M.
Jacyna, M.

Powiązania:

https://bibliotekanauki.pl/articles/247149.pdf

Data publikacji:

2018

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

assignment problem
genetic algorithm
multi-criterion optimization
transportation company

Opis:

The article presents the problem of the task assignment of the vehicles for the transportation company, which deals with the transport of the cargo in the full truckload system. The presented problem is a complex decision making issue which has not been analysed in the literature before. There must be passed through two stages in order to solve the task assignment problem of the vehicles for the transportation company. The first stage is to designate the tasks, the other one is to determine the number of the vehicles that perform these tasks. The task in the analysed problem is defined as transporting the cargo from the suppliers to the recipients. The transportation routes of the cargo must be determined. In order to solve the task assignment problem of the vehicles, the genetic algorithm has been developed. The construction stages of this algorithm are presented. The algorithm has been developed to solve the multi-criteria decision problem. What is more, the algorithm is verified by the use of the real input data.

Źródło:

Journal of KONES; 2018, 25, 4; 133-140
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 23.

Tytuł:

Two stage optimization of the PMSM with excitation system composed of different materials

Autorzy:

Knypiński, Ł.
Nowak, L.

Powiązania:

https://bibliotekanauki.pl/articles/97722.pdf

Data publikacji:

2013

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

electric machines
permanent magnet synchronous motor
optimization
genetic algorithm

Opis:

The paper presents the algorithm and software for the optimization of the rotor structure of the permanent magnet synchronous motor with magnet composed of two materials about different magnetic properties. The software consists of two modules: a numerical model of the PMSM motor and an optimization solver. Numerical implementation is based on finite element method. The optimization module has been elaborated employing the Delphi environment. For the rotor structure optimization the genetic algorithm has been applied Selected results of the calculation are presented and discussed.

Źródło:

Computer Applications in Electrical Engineering; 2013, 11; 148-158
1508-4248

Pojawia się w:

Computer Applications in Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 24.

Tytuł:

Optimization of electric and magnetic field intensities in proximity of power lines using genetic and particle swarm algorithms

Autorzy:

Król, K.
Machczyński, W.

Powiązania:

https://bibliotekanauki.pl/articles/141588.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

power line
electric field
magnetic field
optimization
genetic algorithm
particle swarm algorithm

Opis:

The paper presents optimization of power line geometrical parameters aimed to reduce the intensity of the electric field and magnetic field intensity under an overhead power line with the use of a genetic algorithm (AG) and particle swarm optimization (PSO). The variation of charge distribution along the conductors as well as the sag of the overhead line and induced currents in earth wires were taken into account. The conductor sag was approximated by a chain curve. The charge simulation method (CSM) and the method of images were used in the simulations of an electric field, while a magnetic field were calculated using the Biot–Savart law. Sample calculations in a three-dimensional system were made for a 220 kV single – circuit power line. A comparison of the used optimization algorithms was made.

Źródło:

Archives of Electrical Engineering; 2018, 67, 4; 829-843
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 25.

Tytuł:

Multi-objective design optimization of five-phase fractional-slot concentrated-winding surface-mounted permanent-magnet machine

Autorzy:

Nekoubin, Amir
Soltani, Jafar
Dowlatshah, Milad

Powiązania:

https://bibliotekanauki.pl/articles/949883.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

Finite Element Method
genetic algorithm
optimization
permanent-magnet motors

Opis:

The multi-phase permanent-magnet machines with a fractional-slot concentratedwinding (FSCW) are a suitable choice for certain purposes like aircraft, marine, and electric vehicles, because of the fault tolerance and high power density capability. The paper aims to design, optimize and prototype a five-phase fractional-slot concentrated-winding surface-mounted permanent-magnet motor. To optimize the designed multi-phase motor a multi-objective optimization technique based on the genetic algorithm method is applied. The machine design objectives are to maximize torque density of the motor and maximize efficiency then to determine the best choice of the designed machine parameters. Then, the two-dimensional Finite Element Method (2D-FEM) is employed to verify the performance of the optimized machine. Finally, the optimized machine is prototyped. The paper found that the results of the prototyped machine validate the results of theatrical analyses of the machine and accurate consideration of the parameters improved the acting of the machine.

Źródło:

Archives of Electrical Engineering; 2020, 69, 4; 873-889
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 26.

Tytuł:

A hybrid ant colony for multiresponse mixed-integer problems

Autorzy:

Kushwaha, S.
Mukherjee, I.

Powiązania:

https://bibliotekanauki.pl/articles/409419.pdf

Data publikacji:

2012

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

ant colony optimization(ACO)
desirability functions
genetic algorithm (GA)
multiple response optimization(MRO)

Opis:

In this paper, a hybrid ant colony optimization (ACO) is used to solve a multiple response optimization problem with mixed-integer (MI) search space. The work reported in this paper may be clasified into three part. The first part discusses on relevant litratures and the methodology to solve multiple response optimization problem. The second part provide details on the working principal, parameter tuning of a hybrid ACO proposed for mixed integer state space. In the hybrid ACO, genetic algorithm (GA) is used for intensification of the search strategy. Standard single response (objective) test functions are selected to verify the suitability of hybrid ACO. The third part of this research work illustrates the application of the hybrid ACO in a multiple response optimization (MRO) problem. Statistical experimentation, partial least square regression, 'maximin' desirability function, and hybrid ACO is used to solve the MRO problem. The results confirm the suitability of the hybid ACO for a typical MI MRO problem.

Źródło:

Research in Logistics & Production; 2012, 2, 4; 317-327
2083-4942
2083-4950

Pojawia się w:

Research in Logistics & Production

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 27.

Tytuł:

The Use of a Global Index of Acoustic Assessment for Predicting Noise in Industrial Rooms and Optimizing the Location of Machinery and Workstations

Autorzy:

Pleban, D.

Powiązania:

https://bibliotekanauki.pl/articles/89745.pdf

Data publikacji:

2014

Wydawca:

Centralny Instytut Ochrony Pracy

Tematy:

noise
machinery
optimization
genetic algorithm
hałas
maszyny
optymalizacja
algorytm genetyczny

Opis:

This paper describes the results of a study aimed at developing a tool for optimizing the location of machinery and workstations. A global index of acoustic assessment of machines was developed for this purpose. This index and a genetic algorithm were used in a computer tool for predicting noise emission of machines as well as optimizing the location of machines and workstations in industrial rooms. The results of laboratory and simulation tests demonstrate that the developed global index and the genetic algorithm support measures aimed at noise reduction at workstations.

Źródło:

International Journal of Occupational Safety and Ergonomics; 2014, 20, 4; 627-638
1080-3548

Pojawia się w:

International Journal of Occupational Safety and Ergonomics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 28.

Tytuł:

Simultaneous optimization of flotation column performance using genetic evolutionary algorithm

Autorzy:

Nakhaei, F.
Irannajad, M.
Yousefikhoshbakht, M.

Powiązania:

https://bibliotekanauki.pl/articles/110806.pdf

Data publikacji:

2016

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

flotation column
optimization
genetic algorithm
non-linear regression
upgrading curve

Opis:

Column flotation is a multivariable process. Its optimization guarantees the metallurgical yield of the process, expressed by the grade and recovery of the concentrate. The present work aimed at applying genetic algorithms (GAs) to optimize a pilot column flotation process which is characterized by being difficult to be optimized via conventional methods. A non-linear mathematical model was used to describe the dynamic behavior of the multivariable process. The solution of the optimization problem using conventional algorithms does not always lead to convergence because of the high dimensionality and non-linearity of the model. In order to deal with this process, the use of a genetic evolutionary algorithm is justified. In this way, GA was coupled with the multivariate non-linear regression (MNLR) of the column flotation metallurgical performance as a fitting function in order to optimize the column flotation process. Then, this kind of intelligent approach was verified by using mineral processing approaches such as Halbich’s upgrading curve. The aim of the optimization through GAs was searching for the process inputs that maximize the productivity of copper in the Sarcheshmeh pilot plant. In this case, the simulation optimization problem was defined as finding the best values for the froth height, chemical reagent dosage, wash water, air flow rate, air holdup, and Cu grade in rougher and column feed streams. The results indicated that GA was a robust and powerful search method to find the best values of the flotation column model parameters that lead to more reliable simulation predictions at a reasonable time. Based on the grade–recovery Halbich upgrading curve, the MNLR model coupled with GA can be used for determination of the flotation optimum conditions.

Źródło:

Physicochemical Problems of Mineral Processing; 2016, 52, 2; 874-893
1643-1049
2084-4735

Pojawia się w:

Physicochemical Problems of Mineral Processing

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 29.

Tytuł:

Numerical analysis of tailing dam with calibration based on genetic algorithm and geotechnical monitoring data

Autorzy:

Grosel, Szczepan

Powiązania:

https://bibliotekanauki.pl/articles/1845160.pdf

Data publikacji:

2021

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

soil parameters
optimization
slope stability
genetic algorithm
observational method
monitoring

Opis:

The article presents a method of calibration of material parameters of a numerical model based on a genetic algorithm, which allows to match the calculation results with measurements from the geotechnical monitoring network. This method can be used for the maintenance of objects managed by the observation method, which requires continuous monitoring and design alterations. The correctness of the calibration method has been verified on the basis of artificially generated data in order to eliminate inaccuracies related to approximations resulting from the numerical model generation. Using the example of the tailing dam model the quality of prediction of the selected measurement points was verified. Moreover, changes of factor of safety values, which is an important indicator for designing this type of construction, were analyzed. It was decided to exploit the case of dam of reservoir, which is under continuous construction, that is dam height is increasing constantly, because in this situation the use of the observation method is relevant.

Źródło:

Studia Geotechnica et Mechanica; 2021, 43, 1; 34-47
0137-6365
2083-831X

Pojawia się w:

Studia Geotechnica et Mechanica

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 30.

Tytuł:

Quality improvement of a gear transmission by means of genetic algorithm

Autorzy:

Lempa, Paweł
Lisowski, Edward
Masui, Fumito
Filo, Grzegorz
Ptaszynski, Michal
Domagała, Mariusz
Fabiś-Domagała, Joanna

Powiązania:

https://bibliotekanauki.pl/articles/104043.pdf

Data publikacji:

2019

Wydawca:

Stowarzyszenie Menedżerów Jakości i Produkcji

Tematy:

optimization
genetic algorithm
gear transmission
optymalizacja
algorytm genetyczny
przekładnia zębata

Opis:

The article deals with the issue of quality improvement of a gear transmission by optimizing its geometry with the use of genetic algorithms. The optimization method is focused on increasing productivity and efficiency of the pump and reducing its pulsation. The best results are tested on mathematical model and automatically modelled in 3D be means of PTC Creo Software. The developed solution proved to be an effective tool in the search for better results, which greatly improved parameters of pump especially reduced flow pulsation.

Źródło:

Quality Production Improvement - QPI; 2019, 1, 1; 386-393
2657-8603

Pojawia się w:

Quality Production Improvement - QPI

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Optimal design of sandwich panels with a soft core
Optymalne projektowanie płyt warstwowych z miękkim rdzeniem

Autorzy:

Studziński, R.
Pozorski, Z.
Garstecki, A.

Powiązania:

https://bibliotekanauki.pl/articles/279446.pdf

Data publikacji:

2009

Wydawca:

Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej

Tematy:

sandwich panels
soft-core
Pareto optimization
soft computing
genetic algorithm

Opis:

The main issue taken up in the paper is to find optimal designs of multispan sandwich panels with slightly profiled steel facings and polyurethane foam core (PUR), which would satisfy conflicting demands of the market, i.e. minimal variance in types of panels, maximum range of application and minimum cost. The aim is to find dimensional and material parameters of panels which generate minimum cost and maximum length of span under prescribed loads in ultimate and serviceability limit states. The multi-criterion optimization problem is formulated in such a way, where the length of the span plays two roles, namely a design variable and a component of a vector objective function. An evolutionary algorithm is used. Numerous inequality constraints are introduced in two ways: directly and by external penalty functions.
Wpracy podejmuje się problem optymalizacji wieloprzęsłowych płyt warstwowych z rdzeniem z poliuretanu (PUR) i okładzinami stalowymi lekko profilowanymi. Poszukuje się rozwiązań, które spełnią sprzeczne wymagania rynku, mianowicie: minimalizację typoszeregu płyt, maksymalizację zakresu ich zastosowania oraz minimalizację kosztu produkcji. Celem optymalizacji jest znalezienie parametrów geometrycznych i materiałowych płyt warstwowych, które minimalizują koszt oraz maksymalizują dopuszczalną rozpiętość dla ustalonych obciążeń i przy spełnieniu stanów granicznych nośności i użytkowalności. W wielokryterialnym sformułowaniu problemu optymalizacyjnego rozpiętość pełni dwie funkcje. Jest ona równocześnie zmienną projektową i składową wektora funkcji celu. Jako narzędzie optymalizacji wykorzystano algorytmy genetyczne. Ograniczenia nierównościowe wprowadzono do procedury optymalizacyjnej za pomocą zewnętrznej funkcji kary oraz jawnie.

Źródło:

Journal of Theoretical and Applied Mechanics; 2009, 47, 3; 685-699
1429-2955

Pojawia się w:

Journal of Theoretical and Applied Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

Tytuł:

The Use of Heuristic Algorithms to Optimize the Transport Issues on the Example of Municipal Services Companies

Autorzy:

Izdebski, M.

Powiązania:

https://bibliotekanauki.pl/articles/223579.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

municipal services companies
transport
optimization
genetic algorithm
ant algorithm
usługi komunalne
optymalizacja
algorytm genetyczny

Opis:

In this article the main optimization problems in the municipal services companies were presented. These problems concern the issue of vehicle routing. The mathematical models of these problems were described. The function of criterion and the conditions on designating the vehicle routing were defined. In this paper the hybrid algorithm solving the presented problems was proposed. The hybrid algorithm consists of two heuristic algorithms: the ant and the genetic algorithm. In this paper the stages of constructing of the hybrid algorithm were presented. A structure of the data processed by the algorithm, a function of adaptation, a selection of chromosomes, a crossover, a mutation and an inversion were characterized. A structure of the data was presented as string of natural numbers. In selection process the roulette method was used and in the crossover process the operator PMX was presented. This algorithm was verified in programming language C #. The process of verification was divided into two stages. In the first stage the best parameters of the hybrid algorithm were designated. In the second stage the algorithm was started with these parameters and the result was compared with the random search algorithm. The random search algorithm generates 2000 routes and the best result is compared with the hybrid algorithm.

Źródło:

Archives of Transport; 2014, 29, 1; 27-36
0866-9546
2300-8830

Pojawia się w:

Archives of Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 33.

Tytuł:

A genetic algorithm and B&B algorithm for integrated production scheduling, preventiveand corrective maintenance to save energy

Autorzy:

Sadiqi, Assia
El Abbassi, Ikram
El Barkany, Abdellah
Darcherif, Moumen
El Biyaali, Ahmed

Powiązania:

https://bibliotekanauki.pl/articles/1841396.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

scheduling
maintenance
genetic algorithm
branch
bound
MILP
modeling
optimization
CPLEX
Python

Opis:

The rapid global economic development of the world economy depends on the availability of substantial energy and resources, which is why in recent years a large share of non-renewable energy resources has attracted interest in energy control. In addition, inappropriate use of energy resources raises the serious problem of inadequate emissions of greenhouse effect gases, with major impact on the environment and climate. On the other hand, it is important to ensure efficient energy consumption in order to stimulate economic development and preserve the environment. As scheduling conflicts in the different workshops are closely associated with energy consumption. However, we find in the literature only a brief work strictly focused on two directions of research: the scheduling with PM and the scheduling with energy. Moreover, our objective is to combine both aspects and directions of in-depth research in a single machine. In this context, this article addresses the problem of integrated scheduling of production, preventive maintenance (PM) and corrective maintenance (CM) jobs in a single machine. The objective of this article is to minimize total energy consumption under the constraints of system robustness and stability. A common model for the integration of preventive maintenance (PM) in production scheduling is proposed, where the sequence of production tasks, as well as the preventive maintenance (PM) periods and the expected times for completion of the tasks are established simultaneously; this makes the theory put into practice more efficient. On the basis of the exact Branch and Bound method integrated on the CPLEX solver and the genetic algorithm (GA) solved in the Python software, the performance of the proposed integer binary mixed programming model is tested and evaluated. Indeed, after numerically experimenting with various parameters of the problem, the B&B algorithm works relatively satisfactorily and provides accurate results compared to the GA algorithm. A comparative study of the results proved that the model developed was sufficiently efficient.

Źródło:

Management and Production Engineering Review; 2020, 11, 4; 138-148
2080-8208
2082-1344

Pojawia się w:

Management and Production Engineering Review

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 34.

Tytuł:

A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm

Autorzy:

Dziwiński, Piotr
Bartczuk, Łukasz
Paszkowski, Józef

Powiązania:

https://bibliotekanauki.pl/articles/1837533.pdf

Data publikacji:

2020

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

hybrid methods
Particle Swarm Optimization
Genetic Algorithm
fuzzy systems
multimodal function

Opis:

The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2020, 10, 2; 95-111
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 35.

Tytuł:

GPU-based tuning of quantum-inspired genetic algorithm for a combinatorial optimization problem

Autorzy:

Nowotniak, R.
Kucharski, J.

Powiązania:

https://bibliotekanauki.pl/articles/201268.pdf

Data publikacji:

2012

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

quantum-inspired genetic algorithm
evolutionary computing
meta-optimization
parallel algorithms
GPGPU

Opis:

This paper concerns efficient parameters tuning (meta-optimization) of a state-of-the-art metaheuristic, Quantum-Inspired Genetic Algorithm (QIGA), in a GPU-based massively parallel computing environment (NVidia CUDATMtechnology). A novel approach to parallel implementation of the algorithm has been presented. In a block of threads, each thread transforms a separate quantum individual or different quantum gene; In each block, a separate experiment with different population is conducted. The computations have been distributed to eight GPU devices, and over 400× speedup has been gained in comparison to Intel Core i7 2.93GHz CPU. This approach allows efficient meta-optimization of the algorithm parameters. Two criteria for the meta-optimization of the rotation angles in quantum genes state space have been considered. Performance comparison has been performed on combinatorial optimization (knapsack problem), and it has been presented that the tuned algorithm is superior to Simple Genetic Algorithm and to original QIGA algorithm.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2012, 60, 2; 323-330
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

Performance Comparison of Optimization Methods for Flat-Top Sector Beamforming in a Cellular Network

Autorzy:

Nandi, Pampa
Roy, Jibendu Sekhar

Powiązania:

https://bibliotekanauki.pl/articles/2142316.pdf

Data publikacji:

2022

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

flat-top sector beam
particle swarm optimization
real-coded genetic algorithm

Opis:

The flat-top radiation pattern is necessary to form an appropriate beam in a sectored cellular network and to pro vide users with best quality services. The flat-top pattern offers sufficient power and allows to minimize spillover of signal to adjacent sectors. The flat-top sector beam pattern is relied upon In sectored cellular networks, in multiple-input multiple-output (MIMO) systems and ensures a nearly constant gain in the desired cellular sector. This paper presents a comparison of such optimization techniques as real-coded genetic algorithm (RGA) and particle swarm optimization (PSO), used in cellular networks in order to achieve optimum flat-top sector patterns. The individual parameters of flat-top sector beams, such as cellular coverage, ripples in the flat-top beam, spillover of radiation to the adjacent sectors and side lobe level (SLL) are investigated through optimization performed for 40◦ and 60◦ sectors. These parameters are used to compare the performance of the optimized RGA and PSO algorithms. Overall, PSO outperforms the RGA algorithm.

Źródło:

Journal of Telecommunications and Information Technology; 2022, 3; 39--46
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

Optimization of the impact multi-mass vibration absorbers

Autorzy:

Kernytskyy, I.
Diveyev, B.
Horbaj, O.
Hlobchak, M.
Kopytko, M.
Zachek, O.

Powiązania:

https://bibliotekanauki.pl/articles/886928.pdf

Data publikacji:

2017

Wydawca:

Szkoła Główna Gospodarstwa Wiejskiego w Warszawie. Wydawnictwo Szkoły Głównej Gospodarstwa Wiejskiego w Warszawie

Tematy:

impact damping system
multi-mass
dynamic vibration absorber
optimization
simultaneous optimization
genetic algorithm
Boltzmann approximation

Opis:

Optimization of the impact multi-mass vibration absorbers. The problem of attaching dynamic vibration absorber (DVA) to a discrete multi-degree-of-freedom or continuous structure has been outlined in many papers and monographs. An impact damping system can overcome some limitations by impact as the damping medium and impact mass interaction as the damping mechanism. The paper contemplates the provision of DVA with the several of the impact masses. Such originally designed absorbers reduce vibration selectively in maximum vibration mode without introducing vibration in other modes. An impact damper is a passive control device which takes the form of a freely moving mass, constrained by stops attached to the structure under control, i.e. the primary structure. The damping results from the exchange of momentum during impacts between the mass and the stops as the structure vibrates. The paper contemplates the provision of the impact multi-mass DVA’s with masses collisions for additional damping. For some cases of DVA optimization such a design seems more effective than conventional multi-mass DVA with independent mass moving. A technique is developed to give the optimal DVA’s for the elimination of excessive vibration in harmonic stochastic and impact loaded systems.

Źródło:

Scientific Review Engineering and Environmental Sciences; 2017, 26, 3[77]
1732-9353

Pojawia się w:

Scientific Review Engineering and Environmental Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

Tytuł:

Stacking sequence optimization of composite beams with different layer thicknesses

Autorzy:

Karaçam, F
Timarci, T

Powiązania:

https://bibliotekanauki.pl/articles/102025.pdf

Data publikacji:

2015

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

laminated composite beams
static analysis
genetic algorithm
layer thickness
stacking sequence optimization

Opis:

In this study, stacking sequence optimization of composite beams with different layer thicknesses is investigated for various boundary conditions. A unified shear deformation theory is used for analytical solution. The optimization process is carried out in order to obtain the minimum deflection parameters for Clamped-Free (C-F), Clamped-Clamped (C-C) and simply supported (S-S) boundary conditions under a uniform distributed load by use of genetic algorithm for a specific number of population and generation. Finally, among all possible combinations of layer thicknesses, the one giving the minimum deflection parameter and corresponding stacking sequence is chosen. The minimum values and corresponding stacking sequences are presented for different boundary conditions.

Źródło:

Advances in Science and Technology. Research Journal; 2015, 9, 26; 7-11
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 39.

Tytuł:

Use Of Genetic Algorithms in Supply Chain Management. Literature Review and Current Trends
Использование генетических алгоритмов в управлении цепью поставок. Обзор литературы, актуальные тренды

Autorzy:

Stawiński, Piotr

Powiązania:

https://bibliotekanauki.pl/articles/1195683.pdf

Data publikacji:

2013

Wydawca:

Szkoła Główna Handlowa w Warszawie

Tematy:

управление цепью поставок
генетический алгоритм
оптимизация
supply chain management
genetic algorithm
optimization

Opis:

For the past few decades SCM has been one of the main objectives in research and practice. Since that time researchers have developed a lot of methods and procedures which optimized this process. To create an efficient supply chain network the resources and factories must be tightly integrated. The most supply chain network designs have multiple layers, members, periods, products, and comparative resources constraints exist between different layers. Supply chain networks design is related to the problems which are very popular in literature. The subject of this paper is to present the variants, configurations and parameters of genetic algorithm (GA) for solving supply chain network design problems. We focus on references from 2000 to 2011. Furthermore, current trends are introduced and discussed.
Уже несколько десятилетий управление цепью поставок (SCM) является одним из главных направлений исследований и практики. За это время были разработаны многие методы и процедуры оптимизации этого процесса. Чтобы создать эффективную сеть цепи поставок ресурсы и фабрики должны быть тесно интегрированы. SCM обращается к проблемам, которые очень популярны в литературе. Предметом настоящей работы является презентация вариантов конфигурации и параметров генетического алгоритма (GA), используемого для решения проблем с цепью поставок. В настоящей статье представлен обзор литературы за 2000–2011 гг. Креме того обсуждены здесь тренды, касающиеся проблематики SCM.

Źródło:

Edukacja Ekonomistów i Menedżerów; 2013, 27, 1; 167-184
1734-087X

Pojawia się w:

Edukacja Ekonomistów i Menedżerów

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 40.

Tytuł:

Acoustical Assessment of Automotive Mufflers Using FEM, Neural Networks, and a Genetic Algorithm

Autorzy:

Chang, Y.-C.
Chiu, M.-C.
Wu, M.-R.

Powiązania:

https://bibliotekanauki.pl/articles/177901.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

acoustics
finite element method
genetic algorithm
muffler optimization
polynomial neural network model

Opis:

In order to enhance the acoustical performance of a traditional straight-path automobile muffler, a multi-chamber muffler having reverse paths is presented. Here, the muffler is composed of two internally parallel/extended tubes and one internally extended outlet. In addition, to prevent noise transmission from the muffler’s casing, the muffler’s shell is also lined with sound absorbing material. Because the geometry of an automotive muffler is complicated, using an analytic method to predict a muffler’s acoustical performance is difficult; therefore, COMSOL, a finite element analysis software, is adopted to estimate the automotive muffler’s sound transmission loss. However, optimizing the shape of a complicated muffler using an optimizer linked to the Finite Element Method (FEM) is time-consuming. Therefore, in order to facilitate the muffler’s optimization, a simplified mathematical model used as an objective function (or fitness function) during the optimization process is presented. Here, the objective function can be established by using Artificial Neural Networks (ANNs) in conjunction with the muffler’s design parameters and related TLs (simulated by FEM). With this, the muffler’s optimization can proceed by linking the objective function to an optimizer, a Genetic Algorithm (GA). Consequently, the discharged muffler which is optimally shaped will improve the automotive exhaust noise.

Źródło:

Archives of Acoustics; 2018, 43, 3; 517-529
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 41.

Tytuł:

The concept of genetic programming in organizing internal transport processes

Autorzy:

Lewczuk, K.

Powiązania:

https://bibliotekanauki.pl/articles/223845.pdf

Data publikacji:

2015

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

scheduling
internal transport process
optimization
genetic algorithm
transport wewnętrzny
optymalizacja
programowanie genetyczne

Opis:

The paper presents proposition of using genetic algorithm to support organization of internal transport processes in logistics facilities. The organization of internal transport can be done through solving optimization task of scheduling internal transport process with allocation of human resources and equipment to the tasks. Internal transport process was defined and discussed as an object of organization. Precise methods of solving proposed optimization task were unable to give useful solutions according to the computational complexity of the problem, so heuristic genetic algorithm was proposed. The possible structures of chromosome representing feasible solutions, methods of generating initial population, base genetic operators: selection and inheritance, crossover, mutation and fixing of individuals were described. The main implementation difficulties, computational experiments and the scope of application of the algorithm were discussed.

Źródło:

Archives of Transport; 2015, 34, 2; 61-74
0866-9546
2300-8830

Pojawia się w:

Archives of Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 42.

Tytuł:

Optimization of slender systems by means of genetic algorithms

Autorzy:

Sokół, K.
Kulawik, A.

Powiązania:

https://bibliotekanauki.pl/articles/973636.pdf

Data publikacji:

2014

Wydawca:

Politechnika Częstochowska. Wydawnictwo Politechniki Częstochowskiej

Tematy:

crack
genetic algorithm
optimization
slender system
pęknięcie
algorytm genetyczny
optymalizacja
układ smukły

Opis:

In this paper, the results of numerical studies on optimization of a geometrically nonlinear column with an internal crack by means of genetic algorithms are presented. The system is loaded by an axially applied external force P with a constant line of action. The presented problem is formulated on the basis of the principle of stationary total potential energy. The main purpose of this paper is to investigate an influence upon the localization of the crack and flexural rigidity ratio on critical loading of the system and to find an optimum localization of the crack in order to achieve high loading capacity. In order to calculate optimum values of these parameters the genetic algorithms are implemented into computer program. The artificial method of solution of the problem has been used due to the strongly nonlinear nature of the investigated problem.

Źródło:

Journal of Applied Mathematics and Computational Mechanics; 2014, 13, 1; 115-124
2299-9965

Pojawia się w:

Journal of Applied Mathematics and Computational Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 43.

Tytuł:

Some efficient algorithms to deal with redundancy allocation problems

Autorzy:

Es-Sadqi, Mustapha
Idrissi, Abdellah
Benhassine, Ahlem

Powiązania:

https://bibliotekanauki.pl/articles/2141899.pdf

Data publikacji:

2020

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

redundancy allocation problem
constraint programming
forward checking
optimization
genetic algorithm
top_k

Opis:

In this paper, we will discuss some algorithms in order to better optimize the problems of redundancy allocation in multi-state systems. The goal is to find the optimal configuration of the system that maximizes the availability and minimizes the investment cost. The availability will be evaluated using the universal generating function. In first step, our contribution consists in improving the genetic algorithm. In a second step, in the framework of the Constraint Programming, we propose a new method of optimization based on the Forward Checking as solver. Finally, we used the top-k method in our choice that helps us to get the best k elements from all possible values with highest availability. In comparison with the chosen study, our methods yield better results that satisfy the constraints of the problem in a shorter time.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 4; 48-57
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 44.

Tytuł:

Evolutionary computing approaches to optimum design of fuzzy logic controller for a flexible robot system

Autorzy:

Subudhi, B.
Ranasingh, S.

Powiązania:

https://bibliotekanauki.pl/articles/230107.pdf

Data publikacji:

2013

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

flexible manipulator
fuzzy logic
genetic algorithm
bacteria foraging optimization
tip position tracking

Opis:

This paper presents the design of a Fuzzy Logic Controller (FLC) whose parameters are optimized by using Genetic Algorithm (GA) and Bacteria Foraging Optimization (BFO) for tip position control of a single link flexible manipulator. The proposed FLC is designed by minimizing the fitness function, which is defined as a function of tip position error, through GA and BFO optimization algorithms achieving perfect tip position tracking of the single link flexible manipulator. Then the tip position responses obtained by using both the above controllers are compared to suggest the best controller for the tip position tracking.

Źródło:

Archives of Control Sciences; 2013, 23, 4; 395-412
1230-2384

Pojawia się w:

Archives of Control Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 45.

Tytuł:

Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm. Part II. Analysis of the results

Autorzy:

Sekulski, Z.

Powiązania:

https://bibliotekanauki.pl/articles/260079.pdf

Data publikacji:

2011

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

ship structure
multi-objective optimization
evolutionary algorithm
genetic algorithm
Pareto domination
set of non-dominated solutions

Opis:

Real ship structural design problems are usually characterized by presence of many conflicting objectives. Simultaneously, a complete definition of the optimum structural design requires a formulation of size-topology-shape-material optimization task unifying the optimization problems from the four areas and giving an effective solution of the problem. Any significant progress towards solving the problem has not been obtained so far. An objective of the present paper was to develop an evolutionary algorithm for multiobjective optimization of the structural elements of large spatial sections of ships. Selected elements of the multi-criteria optimization theory have been presented in detail. Methods for solution of the multi-criteria optimization problems have been discussed with the focus on the evolutionary optimization algorithms. In the paper an evolutionary algorithm where selection takes place based on the aggregated objective function combined with domination attributes as well as distance to the asymptotic solution, is proposed and applied to solve the problem of optimizing structural elements with respect to their weight and surface area for a high - speed vehicle-passenger catamaran structure, with taking into account several design variables such as plate thickness, scantlings of longitudinal stiffeners and transverse frames, and spacing between longitudinal and transversal members. Details of the computational models were kept at the level typical for conceptual design stage. Scantlings were analyzed by using the selected classification society rules. The results of numerical experiments with the use of the developed algorithm are presented. They show that the proposed genetic algorithm may be considered an efficient tool for multi-objective optimization of ship structures. The paper has been published in the three parts: Part I: Theoretical background on evolutionary multiobjective optimization, Part II: Computational simulations, and Part III: Analysis of the results.

Źródło:

Polish Maritime Research; 2011, 4; 3-13
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 46.

Tytuł:

Multi-objective optimization of high speed vehicle-passenger catamaran by genetic algorithm. Part I. Theoretical background on evolutionary multi objective optimization

Autorzy:

Sekulski, Z.

Powiązania:

https://bibliotekanauki.pl/articles/259303.pdf

Data publikacji:

2011

Wydawca:

Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa

Tematy:

ship structure
multi-objective optimization
evolutionary algorithm
genetic algorithm
Pareto domination
set of non-dominated solutions

Opis:

Real ship structural design problems are usually characterized by presence of many conflicting objectives. Simultaneously, a complete definition of the optimal structural design requires a formulation of size-topology-shape-material optimization task unifying the optimization problems from these four areas and giving an effective solution of this problem. So far, a significant progress towards the solution of this problem has not been obtained. An objective of the present paper was to develop an evolutionary algorithm for multi-objective optimization of the structural elements of the large spatial sections of ships. Selected elements of the multi-criteria optimization theory have been presented in details. Methods for solution of the multi-criteria optimization problems have been discussed with the focus on the evolutionary optimization algorithms. In the paper an evolutionary algorithm where selection takes place based on the aggregated objective function combined with domination attributes as well as distance to the asymptotic solution is proposed and applied to solve the problem of optimizing structural elements with respect to their weight and surface area on a high speed vehicle-passenger catamaran structure with several design variables, such as plate thickness, scantlings of longitudinal stiffeners and transverse frames, and spacing between longitudinals and transversal members. Details of the computational models were at the level typical for conceptual design. Scantlings were analyzed using the selected rules of a classification society. The results of numerical experiments with the use of the developed algorithm are presented. They show that the proposed genetic algorithm can be an efficient multi-objective optimization tool for ship structures optimization. The paper will be published in three parts: Part I: Theoretical background on evolutionary multi-objective optimization, Part II: Computational investigations, and Part III: Analysis of the results.

Źródło:

Polish Maritime Research; 2011, 2; 3-18
1233-2585

Pojawia się w:

Polish Maritime Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 47.

Tytuł:

Modelling Microcystis Cell Density in a Mediterranean Shallow Lake of Northeast Algeria (Oubeira Lake), Using Evolutionary and Classic Programming

Autorzy:

Arif, Salah
Djellal, Adel
Djebbari, Nawel
Belhaoues, Saber
Touati, Hassen
Guellati, Fatma Zohra
Bensouilah, Mourad

Powiązania:

https://bibliotekanauki.pl/articles/2174666.pdf

Data publikacji:

2023

Wydawca:

Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH

Tematy:

microcystis cell density
Multiple Linear Regression
Support Vector Machine
Particle Swarm Optimization
Genetic Algorithm
Bird Swarm Algorithm

Opis:

Caused by excess levels of nutrients and increased temperatures, freshwater cyanobacterial blooms have become a serious global issue. However, with the development of artificial intelligence and extreme learning machine methods, the forecasting of cyanobacteria blooms has become more feasible. We explored the use of multiple techniques, including both statistical [Multiple Regression Model (MLR) and Support Vector Machine (SVM)] and evolutionary [Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Bird Swarm Algorithm (BSA)], to approximate models for the prediction of Microcystis density. The data set was collected from Oubeira Lake, a natural shallow Mediterranean lake in the northeast of Algeria. From the correlation analysis of ten water variables monitored, six potential factors including temperature, ammonium, nitrate, and ortho-phosphate were selected. The performance indices showed; MLR and PSO provided the best results. PSO gave the best fitness but all techniques performed well. BSA had better fitness but was very slow across generations. PSO was faster than the other techniques and at generation 20 it passed BSA. GA passed BSA a little further, at generation 50. The major contributions of our work not only focus on the modelling process itself, but also take into consideration the main factors affecting Microcystis blooms, by incorporating them in all applied models.

Źródło:

Geomatics and Environmental Engineering; 2023, 17, 2; 31--68
1898-1135

Pojawia się w:

Geomatics and Environmental Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 48.

Tytuł:

The influence of the diversified passenger population on safe evacuation from a passenger ship
Wpływ zróżnicowania populacji pasażerów na bezpieczną ewakuację ze statku pasażerskiego

Autorzy:

Łozowicka, D.

Powiązania:

https://bibliotekanauki.pl/articles/359307.pdf

Data publikacji:

2010

Wydawca:

Akademia Morska w Szczecinie. Wydawnictwo AMSz

Tematy:

ewakuacja
statki pasażerskie
algorytmy genetyczne
optymalizacja
evacuation modeling
passenger ships
genetic algorithm
optimization

Opis:

This article states theoretical assumptions for the method of identifying disadvantageous evacuation times from ships relating to diversified populations of passengers. The influence of age, sex and physical fitness of people is examined. The presented optimization uses the genetic algorithms method as well as Genetic Algorithm and Direct Search Toolbox included in Matlab software. Examples of calculations of the time of passenger evacuation from a passenger ship are given to verify the operation of the developed method.
W artykule podaje się teoretyczne założenia do metody poszukiwania niekorzystnych czasów ewakuacji ze statków pod kątem zróżnicowania populacji pasażerów. Analizuje się wpływ wieku, płci oraz predyspozycji fizycznych ludzi. Do optymalizacji wykorzystuje się metodę algorytmów genetycznych, a także Genetic Algorithm and Direct Search Toolbox programu Matlab. Podaje się przykładowe obliczenia czasu ewakuacji ze statku pasażerskiego w celu zweryfikowania poprawności działania opracowanej metody.

Źródło:

Zeszyty Naukowe Akademii Morskiej w Szczecinie; 2010, 21 (93); 57-61
1733-8670
2392-0378

Pojawia się w:

Zeszyty Naukowe Akademii Morskiej w Szczecinie

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 49.

Tytuł:

Multi-Criteria 3-Dimension Bin Packing Problem

Autorzy:

Kacprzak, Ł.
Rudy, J.
Żelazny, D.

Powiązania:

https://bibliotekanauki.pl/articles/409522.pdf

Data publikacji:

2015

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

bin packing problem
multi-criteria
genetic algorithm
simulated annealing
discrete optimization
Pareto efficiency

Opis:

In this paper a multi-criteria approach to the 3-dimensions bin packing problem is considered. The chosen maximization criteria are the number and the total volume of the boxes loaded into the container. Existing solution representation and decoding method are applied to the problem. Next, two metaheuristic algorithms, namely simulated annealing and genetic algorithm are developed using the TOPSIS method for solution evaluation. Both algorithms are then used to obtain approximations of the Pareto front for a set of benchmarks from the literature. Despite the fact that both criteria work in favor of each other, we managed to obtain multiple solutions in many cases, proving that lesser number of boxes can lead to better utilization of the container volume and vice versa. We also observed, that the genetic algorithms performs slightly better in our test both in the terms of hyper-volume indicator and number of non-dominated solutions.

Źródło:

Research in Logistics & Production; 2015, 5, 1; 85-94
2083-4942
2083-4950

Pojawia się w:

Research in Logistics & Production

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 50.

Tytuł:

Algorytmy genetyczne w problemach optymalizacji
Genetic algorithms in optimization problems

Autorzy:

Rutczyńska-Wdowiak, K.

Powiązania:

https://bibliotekanauki.pl/articles/250078.pdf

Data publikacji:

2015

Wydawca:

Instytut Naukowo-Wydawniczy TTS

Tematy:

algorytm genetyczny
optymalizacja
funkcja Goldsteina-Price'a
genetic algorithm
optimization
Goldstein-Price function

Opis:

W pracy analizowano skuteczność i uniwersalność stosowania algorytmów genetycznych w wybranych zagadnieniach optymalizacji. Zaimplementowano algorytm genetyczny dla problemu minimalizacji złożonych, trudnych do optymalizacji funkcji Goldsteina-Price'a i funkcji grzbietu wielbłąda sześciogarbnego. Próbowano odpowiedzieć na pytanie, gdzie można stosować omawianą metodę sztucznej inteligencji, a gdzie lepiej zastosować metody klasyczne.
In this work the efficiency and universality of the use of genetic algorithms in selected issues of optimization was analyzed. Genetic algorithm for minimization of Goldstein-Price's function and function of back of camel was implemented. In this work was attempted to answer the question, where can apply this method of artificial intelligence, and where better to use classical methods.

Źródło:

TTS Technika Transportu Szynowego; 2015, 12; 1324-1326, CD
1232-3829
2543-5728

Pojawia się w:

TTS Technika Transportu Szynowego

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 51.

Tytuł:

Complex morlet wavelet design with global parameter optimization for diagnosis of industrial manufacturing faults of tapered roller bearing in noisycondition

Autorzy:

Deák, Krisztián
Kocsis, Imre

Powiązania:

https://bibliotekanauki.pl/articles/329462.pdf

Data publikacji:

2019

Wydawca:

Polska Akademia Nauk. Polskie Towarzystwo Diagnostyki Technicznej PAN

Tematy:

bearing vibration analysis
wavelet
optimization
genetic algorithm
transformacja falkowa
falka
optymalizacja
algorytm genetyczny

Opis:

Detecting manufacturing defects of bearings are difficult because of their unique topography. To find adequate methods for diagnosis is important because they could be responsible for serious problems. Wavelet transform is an efficient tool for analyzing the transients in the vibration signal. In this article we are focusing on industrial grinding faults on the outer ring of tapered roller bearings. Nine different real-valued wavelets, Symlet-2, Symlet-5, Symlet-8, Daubechies (2, 6, 10, 14), Morlet and Meyer wavelets are compared to a designed complex Morlet wavelet according to the Energy-to-Shannon-Entropy ratio criteria to determine which is the most efficient for detecting the manufacturing fault. Parameters of the complex Morlet wavelet are adjustable, thus, it has more flexibility for feature extraction. Genetic algorithm is applied to optimize the center frequency and the bandwidth of the designed wavelet. A sophisticated filtering procedure through multi-resolution analysis is applied with autocorrelation enhancement and envelope detection. To determine the efficiency of the designed wavelet and compare to the other wavelets, a test-rig was constructed equipped with high-precision sensors and devices. The designed wavelet is found to be the most effective to detect the manufacturing fault. Therefore, it has the capacity for an industrial testing procedure.

Źródło:

Diagnostyka; 2019, 20, 2; 77-86
1641-6414
2449-5220

Pojawia się w:

Diagnostyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 52.

Tytuł:

Numerical verification of two-component dental implant in the context of fatigue life for various load cases

Autorzy:

Szajek, K.
Wierszycki, M.

Powiązania:

https://bibliotekanauki.pl/articles/307214.pdf

Data publikacji:

2016

Wydawca:

Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej

Tematy:

implant stomatologiczny
optymalizacja
trwałość zmęczeniowa
algorytm genetyczny
dental implant
optimization
fatigue life
genetic algorithm

Opis:

Purpose: Dental implant designing is a complex process which considers many limitations both biological and mechanical in nature. In earlier studies, a complete procedure for improvement of two-component dental implant was proposed. However, the optimization tasks carried out required assumption on representative load case, which raised doubts on optimality for the other load cases. This paper deals with verification of the optimal design in context of fatigue life and its main goal is to answer the question if the assumed load scenario (solely horizontal occlusal load) leads to the design which is also “safe” for oblique occlussal loads regardless the angle from an implant axis. Methods: The verification is carried out with series of finite element analyses for wide spectrum of physiologically justified loads. The design of experiment methodology with full factorial technique is utilized. All computations are done in Abaqus suite. Results: The maximal Mises stress and normalized effective stress amplitude for various load cases are discussed and compared with the assumed “safe” limit (equivalent of fatigue life for 5e6 cycles). Conclusions: The obtained results proof that coronial-appical load component should be taken into consideration in the two component dental implant when fatigue life is optimized. However, its influence in the analyzed case is small and does not change the fact that the fatigue life improvement is observed for all components within whole range of analyzed loads.

Źródło:

Acta of Bioengineering and Biomechanics; 2016, 18, 1; 103-113
1509-409X
2450-6303

Pojawia się w:

Acta of Bioengineering and Biomechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 53.

Tytuł:

The design of the public transport lines with the use of the fast genetic algorithm
Projektowanie przebiegu linii komunikacji publicznej za pomocą szybkiego algorytmu genetycznego

Autorzy:

Król, A.

Powiązania:

https://bibliotekanauki.pl/articles/361803.pdf

Data publikacji:

2015

Wydawca:

Wyższa Szkoła Logistyki

Tematy:

genetic algorithm
public transport
optimization
bus lines
algorytm genetyczny
transport publiczny
optymalizacja
linie autobusowe

Opis:

Background: The growing role of public transport and the pressure of economic criteria requires the new optimization tools for process of public transport planning. These problems are computationally very complex, thus it is preferable to use various approximate methods, leading to a good solution within an acceptable time. Methods: One of such method is the genetic algorithm mimicking the processes of evolution and natural selection in the nature. In this paper, the different variants of the public transport lines layout are subjected to the artificial selection. The essence of the proposed approach is a simplified method of calculating the value of the fit function for a single individual, which brings relatively short computation time even for large jobs. Results: It was shown that despite the introduced simplifications the quality of the results is not worsened. Using the data obtained from KZK GOP (Communications Municipal Association of Upper Silesian Industrial Region) the described algorithm was used to optimize the layout of the network of bus lines located within the borders of Katowice. Conclusion: The proposed algorithm was applied to a real, very complex network of public transportation and a possibility of a significant improvement of its efficiency was indicated. The obtained results give hope that the presented model, after some improvements can be the basis of the scientific method, and in a consequence of a further development to find practical application.
Wstęp: Rosnąca rola komunikacji publicznej przy jednoczesnym nacisku kryteriów ekonomicznych wymaga zastosowania nowych narzędzi optymalizacyjnych do procesu planowania transportu publicznego. Problemy te są bardzo złożone obliczeniowo, więc korzystne jest zastosowanie różnych metod przybliżonych, prowadzących do uzyskania dobrych rozwiązań w akceptowalnym czasie. Metody: Jedną z takich metod jest algorytm genetyczny, naśladujący procesy ewolucji i doboru naturalnego w przyrodzie. W prezentowanej pracy sztucznemu doborowi podlegają różne warianty układu linii komunikacji publicznej. Istotą proponowanego podejścia jest uproszczony sposób obliczania wartości funkcji dostosowania pojedynczego osobnika, co przynosi stosunkowo krótki czas obliczeń nawet dla dużych zadań. Wyniki: Pokazano, że mimo wprowadzonych uproszczeń, jakość uzyskanych rezultatów nie ulega pogorszeniu. Korzystając z danych uzyskanych od KZK GOP (Komunikacyjny Związek Komunal ny Górnośląskiego Okręgu Przemysłowego) zastosowano opisywany algorytm do optymalizacji układu części sieci linii autobusowych znajdujących się w obrębie miasta Katowice. Wnioski: Zaproponowany algorytm zastosowano do rzeczywistej, bardzo złożonej sieci komunikacji publicznej uzyskując znaczącą poprawę jej efektywności. Otrzymane rezultaty dają nadzieję, że prezentowany model po udoskonaleniu i może być podstawą naukowej metody, a w konsekwencji dalszego rozwoju znaleźć praktyczne zastosowanie.

Źródło:

LogForum; 2015, 11, 3; 275-282
1734-459X

Pojawia się w:

LogForum

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 54.

Tytuł:

Localization in Wireless Sensor Networks Using Heuristic Optimization Techniques

Autorzy:

Niewiadomska-Szynkiewicz, E.
Marks, M.
Kamola, M.

Powiązania:

https://bibliotekanauki.pl/articles/308429.pdf

Data publikacji:

2011

Wydawca:

Instytut Łączności - Państwowy Instytut Badawczy

Tematy:

evolutionary strategy
genetic algorithm
localization
location systems
nonconvex optimization
simulated annealing
wireless sensor network

Opis:

Many applications of wireless sensor networks (WSN) require information about the geographic location of each sensor node. Devices that form WSN are expected to be remotely deployed in large numbers in a sensing field, and to self-organize to perform sensing and acting task. The goal of localization is to assign geographic coordinates to each device with unknown position in the deployment area. Recently, the popular strategy is to apply optimization algorithms to solve the localization problem. In this paper, we address issues associated with the application of heuristic techniques to accurate localization of nodes in a WSN system. We survey and discuss the location systems based on simulated annealing, genetic algorithms and evolutionary strategies. Finally, we describe and evaluate our methods that combine trilateration and heuristic optimization.

Źródło:

Journal of Telecommunications and Information Technology; 2011, 4; 55-64
1509-4553
1899-8852

Pojawia się w:

Journal of Telecommunications and Information Technology

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 55.

Tytuł:

Multiobjective geometry optimization of bldc motor using an evolutionary algorithm
Wielokryterialna optymalizacja geometrii bezszczotkowego silnika prądu stałego z wykorzystaniem algorytmu genetycznego

Autorzy:

Caramia, R
Piotuch, R.
Pałka, R.

Powiązania:

https://bibliotekanauki.pl/articles/1368136.pdf

Data publikacji:

2013

Wydawca:

Sieć Badawcza Łukasiewicz - Instytut Napędów i Maszyn Elektrycznych Komel

Tematy:

synchronous motor
optimization
genetic algorithm
Pareto Front
silnik synchroniczny
optymalizacja
algorytm genetyczny
front Pareto

Opis:

W pracy przedstawiono metodę optymalizacji bezszczotkowego silnika prądu stałego z 4 czteroma biegunami i 24 żłobkami. W szczególności praca koncentruje się na optymalizacji wielokryterialnej z wykorzystaniem algorytmów genetycznych (Optimizaton Toolbox) realizowanych w środowisku Matlab, sprzęgniętym ze środowiskiem Maxwell 14. Matlab został użyty do przeprowadzenia procesu optymalizacji oraz przetwarzania danych liczbowych. Środowisko Maxwell zostało użyte do tworzenia geometrii oraz do przeprowadzenia obliczeń Metodą Elementów Skończonych. Celem pracy była maksymalizacja wartości momentu maksymalnego silnika przy minimalnej masie silnika. Wyniki badań symulacyjnych wykonanych dla modelu 2D pokazały, że sprzęgnięcie obu pakietów obliczeniowych jest możliwe i daje satysfakcjonujące rezultaty. Wykorzystując prosty algorytm genetyczny uzyskano 25% wzrost wartości średniej momentu silnika przy spadku masy silnika o 14%. Otrzymane wyniki zostały poddane weryfikacji z wykorzystaniem modelu 3D.
This paper presents a methodology for the optimization of a Brush Less Direct Current motor (BLDC) with 4 poles and 24 slots. In particular, it is focused on a multiobjective optimization using a genetic algorithm developed in Matlab optimization Toolbox, that is coupled with Maxwell 14. The first one has been used for the optimization and the post-processing of the data, the second one for the Finite Element (FE) analysis and for the geometry creation. Aim of the optimization was to maximize the maximum torque value and minimize the mass of a motor. The simulation results of a 2D model showed that the coupling was possible and give satisfactory results. Using simple genetic algorithm it was possible to increase the average torque value of 25% and lower the mass of the main part of the motor of 14%. Obtained results were verified using a 3D model.

Źródło:

Maszyny Elektryczne: zeszyty problemowe; 2013, 3, 100/1; 89-94
0239-3646
2084-5618

Pojawia się w:

Maszyny Elektryczne: zeszyty problemowe

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 56.

Tytuł:

System reliability optimization: A fuzzy multi-objective genetic algorithm approach
Optymalizacja niezawodności systemu: metoda rozmytego algorytmu genetycznego do optymalizacji wielokryterialnej

Autorzy:

Mutingi, M.

Powiązania:

https://bibliotekanauki.pl/articles/300808.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

system reliability optimization
multi-objective optimization
genetic algorithm
fuzzy optimization
redundancy
optymalizacja niezawodności systemu
optymalizacja wielokryterialna
algorytm genetyczny
optymalizacja rozmyta
nadmiarowość

Opis:

System reliability optimization is often faced with imprecise and conflicting goals such as reducing the cost of the system and improving the reliability of the system. The decision making process becomes fuzzy and multi-objective. In this paper, we formulate the problem as a fuzzy multi-objective nonlinear program. A fuzzy multi-objective genetic algorithm approach (FMGA) is proposed for solving the multi-objective decision problem in order to handle the fuzzy goals and constraints. The approach is able flexible and adaptable, allowing for intermediate solutions, leading to high quality solutions. Thus, the approach incorporates the preferences of the decision maker concerning the cost and reliability goals through the use of fuzzy numbers. The utility of the approach is demonstrated on benchmark problems in the literature. Computational results show that the FMGA approach is promising.
Często spotykanym problemem w optymalizacji niezawodności systemu są niedokładnie określone i sprzeczne cele, takie jak zmniejszenie kosztów systemu przy jednoczesnej poprawie jego niezawodności. Proces podejmowania decyzji staje się wtedy rozmyty i wielokryterialny. W niniejszej pracy, sformułowaliśmy ten problem jako rozmyty wielokryterialny program nieliniowy (FMOOP). Zaproponowaliśmy metodę rozmytego wielokryterialnego algorytmu genetycznego (FMGA), która pozwala rozwiązać wielokryterialny problem decyzyjny z uwzględnieniem rozmytych celów i ograniczeń. Podejście to jest uniwersalne, co pozwala na rozwiązania pośrednie, prowadzące do rozwiązań wysokiej jakości. Metoda uwzględnia preferencje decydenta w zakresie celów związanych z kosztami i niezawodnością poprzez wykorzystanie liczb rozmytych. Użyteczność FMGA wykazano na przykładzie wzorcowych problemów z literatury. Wyniki obliczeń wskazują, że podejście FMGA jest obiecujące.

Źródło:

Eksploatacja i Niezawodność; 2014, 16, 3; 400-406
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 57.

Tytuł:

A logistic optimization for the vehicle routing problem through a case study in the food industry

Autorzy:

Akpinar, Muhammet Enes

Powiązania:

https://bibliotekanauki.pl/articles/1835487.pdf

Data publikacji:

2021

Wydawca:

Wyższa Szkoła Logistyki

Tematy:

vehicle routing problem
time windows
optimization
metaheuristic algorithm
genetic algorithm
trasa pojazdu
okna czasowe
optymalizacja
algorytm metaheurystyczny
algorytm genetyczny

Opis:

In this study, the food delivery problem faced by a food company is discussed. There are seven different regions where the company serves food and a certain number of customers in each region. The time of requesting food for each customer varies according to the shift situation. This type of problem is referred to as a vehicle routing problem with time windows in the literature and the main aim of the study is to minimize the total travel distance of the vehicles. The second aim is to determine which vehicle will follow which route in the region by using the least amount of vehicle according to the desired mealtime. Methods: In this study, genetic algorithm methodology is used for the solution of the problem. Metaheuristic algorithms are used for problems that contain multiple combinations and cannot be solved in a reasonable time. Thus in this study, a solution to this problem in a reasonable time is obtained by using the genetic algorithm method. The advantage of this method is to find the most appropriate solution by trying possible solutions with a certain number of populations. Results: Different population sizes are considered in the study. 1000 iterations are made for each population. According to the genetic algorithm results, the best result is obtained in the lowest population size. The total distance has been shortened by about 14% with this method. Besides, the number of vehicles in each region and which vehicle will serve to whom has also been determined. This study, which is a real-life application, has provided serious profitability to the food company even from this region alone. Besides, there have been improvements at different rates in each of the seven regions. Customers' ability to receive service at any time has maximized customer satisfaction and increased the ability to work in the long term. Conclusions: The method and results used in the study were positive for the food company. However, the metaheuristic algorithm used in this study does not guarantee an optimal result. Therefore, mathematical models or simulation models can be considered in terms of future studies. Besides, in addition to the time windows problem, the pickup problem can also be taken into account and different solution proposals can be developed.

Źródło:

LogForum; 2021, 17, 3; 387-397
1734-459X

Pojawia się w:

LogForum

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 58.

Tytuł:

Multi-criteria reliability optimization for a complex system with a bridge structure in a fuzzy environment: A fuzzy multi-criteria genetic algorithm approach
Wielokryterialna optymalizacja niezawodności złożonego systemu o strukturze mostkowej w środowisku rozmytym. Metoda rozmytego wielokryterialnego algorytmu genetycznego

Autorzy:

Mutingi, M.
Mbohwa, C.
Kommula, V. P.

Powiązania:

https://bibliotekanauki.pl/articles/301750.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN

Tematy:

multi-criteria optimization
reliability optimization
complex bridge system
genetic algorithm
optymalizacja wielokryterialna
optymalizacja niezawodności
złożony system mostkowy
algorytm genetyczny

Opis:

Optimizing system reliability in a fuzzy environment is complex due to the presence of imprecise multiple decision criteria such as maximizing system reliability and minimizing system cost. This calls for multi-criteria decision making approaches that incorporate fuzzy set theory concepts and heuristic methods. This paper presents a fuzzy multi-criteria nonlinear model, and proposes a fuzzy multi-criteria genetic algorithm (FMGA) for complex bridge system reliability design in a fuzzy environment. The algorithm uses fuzzy multi-criteria evaluation techniques to handle fuzzy goals, preferences, and constraints. The evaluation approach incorporates fuzzy preferences and expert choices of the decision maker in regards to cost and reliability goals. Fuzzy evaluation gives the algorithm flexibility and adaptability, yielding near-optimal solutions within short computation times. Results from computational experiments based on benchmark problems demonstrate that the FMGA approach is a more reliable and effective approach than best known algorithm, especially in a fuzzy multi-criteria environment.
Optymalizacja niezawodności systemu w środowisku rozmytym to problem złożony ze względu na konieczność wzięcia pod uwagę wielu niedokładnie określonych kryteriów decyzyjnych, takich jak maksymalizacja niezawodności systemu i minimalizacja kosztów. Wymaga ona zastosowania wielokryterialnych metod podejmowania decyzji, które łączyłyby pojęcia z zakresu teorii zbiorów rozmytych oraz metody heurystyczne. W niniejszej pracy przedstawiono rozmyty wielokryterialny model nieliniowy (FMGA) oraz zaproponowano rozmyty wielokryterialny algorytm genetyczny do projektowania niezawodności złożonych systemów mostkowym w środowisku rozmytym. Algorytm wykorzystuje techniki rozmytej oceny wielokryterialnej do określania rozmytych celów, preferencji oraz ograniczeń. Metoda oceny uwzględnia rozmyte preferencje i eksperckie wybory decydenta dotyczące kosztów oraz celów niezawodnościowych. Ocena rozmyta nadaje algorytmowi cechy elastyczności oraz adaptacyjności, pozwalając na otrzymanie niemal optymalnych rozwiązań w krótkim czasie obliczeniowym. Wyniki eksperymentów obliczeniowych opartych na problemach wzorcowych pokazują, że podejście z zastosowaniem FMGA jest bardziej niezawodne i wydajne niż najbardziej znany algorytm, zwłaszcza w rozmytym środowisku wielokryterialnym.

Źródło:

Eksploatacja i Niezawodność; 2016, 18, 3; 450-456
1507-2711

Pojawia się w:

Eksploatacja i Niezawodność

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 59.

Tytuł:

Dynamics of Stochastic vs. Greedy Heuristics in Traveling Salesman Problem

Autorzy:

Białogłowski, M.
Staniaszek, M.
Laskowski, W.
Grudniak, M.

Powiązania:

https://bibliotekanauki.pl/articles/91276.pdf

Data publikacji:

2018

Wydawca:

Warszawska Wyższa Szkoła Informatyki

Tematy:

traveling salesman problem
Nearest Neighbor
Monte Carlo
Simulated Annealing
Genetic Algorithm
particle swarm optimization (PSO)

Opis:

We studied the relative performance of stochastic heuristics in order to establish the relations between the fundamental elements of their mechanisms. The insights on their dynamics, abstracted from the implementation details, may contribute to the development of an efficient framework for design of new probabilistic methods. For that, we applied four general optimization heuristics with varying number of hyperparameters to traveling salesman problem. A problem-specific greedy approach (Nearest Neighbor) served as a reference for the results of: Monte Carlo, Simulated Annealing, Genetic Algorithm, and Particle Swarm Optimization. The more robust heuristics – with higher configuration potential, i.e. with more hyperparameters – outperformed the smart ones, being surpassed only by the method specifically designed for the task.

Źródło:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki; 2018, 12, 19; 7-24
1896-396X
2082-8349

Pojawia się w:

Zeszyty Naukowe Warszawskiej Wyższej Szkoły Informatyki

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 60.

Tytuł:

Dobór i optymalizacja konfiguracji zasobnika trakcyjnego
Selection and optimization of the traction storage reservoir configuration

Autorzy:

Wieczorek, Maciiej
Lewandowski, Mirosław

Powiązania:

https://bibliotekanauki.pl/articles/34602362.pdf

Data publikacji:

2015

Wydawca:

Sieć Badawcza Łukasiewicz - Poznański Instytut Technologiczny

Tematy:

konfiguracja
optymalizacja
dobór
zasobnik trakcyjny
HESS
algorytm genetyczny
configuration
optimization
selection
traction storage
genetic algorithm

Opis:

W artykule przedstawiono algorytm doboru magazynów energii elektrycznej. Wybór HESS pozwala na spełnienie warunków obciążenia w sposób optymalny. Jednak ustalenie konfiguracji urządzeń w systemie staje się bardzo złożonym zagadnieniem optymalizacyjnym. Przedstawiono propozycje rozwiązania tego problemu z zastosowaniem algorytmu genetycznego.
The article presents an algorithm for selection of storages for electric energy. The choice of HESS allows to meet the load conditions in an optimal way. However, determination of the devices configuration in the system becomes very complex optimization problem. Proposals to solve this problem with the use of a genetic algorithm.

Źródło:

Rail Vehicles/Pojazdy Szynowe; 2015, 4; 55-57
0138-0370
2719-9630

Pojawia się w:

Rail Vehicles/Pojazdy Szynowe

Dostawca treści:

Biblioteka Nauki

/5

Artykuł

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Skocz do pozycji: 61.

Tytuł:

Adaptive robust PID sliding control of a liquid level system based on multi-objective genetic algorithm optimization

Autorzy:

Mahmoodabadi, M. J.
Taherkhorsandi, M.
Talebipour, M.

Powiązania:

https://bibliotekanauki.pl/articles/206697.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

sliding mode control
PID control
adaptive control
genetic algorithm
multi-objective optimization
liquid level system

Opis:

Adaptive robust PID sliding mode control optimized by means of multi-objective genetic algorithm is presented in this paper to control a three-tank liquid level system with external disturbances. While PID constitutes a reliable and stable controller, when compared to sliding mode control (SMC); robustness and tracking performance of SMC are higher than those of the PID control. To use the unique features of both controllers, optimal sliding mode control is executed in terms of a supervisory controller to enhance the performance of optimal adaptive PID control and to provide the necessary control inputs. After the design of the control law, control coefficients of all four involved controllers are optimized by using the multi-objective genetic algorithm so as to minimize errors and the input of the controller. Simulations illustrate that the adaptive robust PID sliding controller based on multi-objective genetic algorithm optimization provides a superior response in comparison to the results obtained separately by PID control, sliding mode control, and adaptive PID control, respectively.

Źródło:

Control and Cybernetics; 2017, 46, 3; 227-246
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 62.

Tytuł:

Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

Autorzy:

Pandiarajan, K.
Babulal, C. K.

Powiązania:

https://bibliotekanauki.pl/articles/141059.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

non-dominated sorting genetic algorithm
generation rescheduling
particle swarm optimization (PSO)
differential evolution
overload index

Opis:

This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem

Źródło:

Archives of Electrical Engineering; 2014, 63, 3; 367-384
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 63.

Tytuł:

Optymalizacja ustawienia paneli PV z wykorzystaniem symulacji - „ClimateStudio” by Solemma
PV panel settings using simulation optimization - Climat Studio by Solemma

Autorzy:

Sitek, Michał

Powiązania:

https://bibliotekanauki.pl/articles/2064148.pdf

Data publikacji:

2022

Wydawca:

PWB MEDIA Zdziebłowski

Tematy:

fotowoltaika
symulacja
optymalizacja
algorytm genetyczny
budynek jednorodzinny
photovoltaics
simulation
optimization
genetic algorithm
one-family building

Opis:

Artykuł to studium przypadku doboru instalacji PV dla domu jednorodzinnego oraz optymalizacji położenia paneli na dachu płaskim z wykorzystaniem narzędzi projektowania generatywnego i optymalizacji genetycznej. Celem przeprowadzonych symulacji było wykazanie przydatności wybranego narzędzia do analiz zmiennych projektowanego systemu w relacji do zapotrzebowania na energię zdefiniowanego przez obecność i aktywności użytkowników obiektu.
This paper describes a case study of the selection of a PV installation for a single-family house and its configuration on a flat roof, using generative design and genetic optimisation tools. The purpose of the simulations carried out was to demonstrate the suitability of the chosen tool for the analysis of the variables of the designed system in relation to the energy demand defined by the presence and activity of the users of the facility.

Źródło:

Builder; 2022, 26, 3; 74--78
1896-0642

Pojawia się w:

Builder

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 64.

Tytuł:

Genetic and combinatorial algorithms for optimal sizing and placement of active power filters

Autorzy:

Maciążek, M.
Grabowski, D.
Pasko, M.

Powiązania:

https://bibliotekanauki.pl/articles/330809.pdf

Data publikacji:

2015

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

power quality
optimization
active power filter
harmonics
genetic algorithm
combinatorial algorithm
jakość energii
energetyczny filtr aktywny
algorytm genetyczny
algorytm kombinatoryczny

Opis:

The paper deals with cost effective compensator placement and sizing. It becomes one of the most important problems in contemporary electrical networks, in which voltage and current waveform distortions increase year-by-year reaching or even exceeding limit values. The suppression of distortions could be carried out by means of three types of compensators, i.e., passive filters, active power filters and hybrid filters. So far, passive filters have been more popular mainly because of economic reasons, but active and hybrid filters have some advantages which should cause their wider application in the near future. Active power filter placement and sizing could be regarded as an optimization problem. A few objective functions have been proposed for this problem. In this paper we compare solutions obtained by means of combinatorial and genetic approaches. The theoretical discussion is followed by examples of active power filter placement and sizing.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2015, 25, 2; 269-279
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 65.

Tytuł:

A factor graph based genetic algorithm

Autorzy:

Helmi, B. H.
Rahmani, A. T.
Pelikan, M.

Powiązania:

https://bibliotekanauki.pl/articles/330811.pdf

Data publikacji:

2014

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

optimization problem
genetic algorithm
estimation
distribution algorithm
factor graph
matrix factorization
problem optymalizacji
algorytm genetyczny
algorytm estymacji rozkładu
faktoryzacja macierzy

Opis:

We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments show that FGGA is capable of learning linkages and solving the optimization problems in polynomial time with a polynomial number of evaluations.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2014, 24, 3; 621-633
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 66.

Tytuł:

Metamodel-Based Optimization of the Labyrinth Seal

Autorzy:

Rulik, S.
Wróblewski, W.
Frączek, D.

Powiązania:

https://bibliotekanauki.pl/articles/140287.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

labyrinth seal
metamodel optimization
neural network
genetic algorithm
evolutionary algorithm
CFD optimization
uszczelnienie labiryntowe
optymalizacja oparta na metamodelu
sieć neuronowa
algorytm genetyczny
algorytm ewolucyjny
optymalizacja CFD

Opis:

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.

Źródło:

Archive of Mechanical Engineering; 2017, LXIV, 1; 75-91
0004-0738

Pojawia się w:

Archive of Mechanical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 67.

Tytuł:

Weight minimization of spatial trusses with genetic algorithm

Autorzy:

Grzywiński, Maksym
Selejdak, Jacek

Powiązania:

https://bibliotekanauki.pl/articles/103965.pdf

Data publikacji:

2019

Wydawca:

Stowarzyszenie Menedżerów Jakości i Produkcji

Tematy:

weight minimization
space truss
size optimization
shape optimization
genetic algorithm
minimalizacja wagi
kratownica przestrzenna
optymalizacja wielkości
optymalizacja kształtu
algorytm genetyczny

Opis:

A genetic algorithm is proposed to solve the weight minimization problem of spatial truss structures considering size and shape design variables. A very recently developed metaheuristic method called JAYA algorithm (JA) is implemented in this study for optimization of truss structures. The main feature of JA is that it does not require setting algorithm specific parameters. The algorithm has a very simple formulation where the basic idea is to approach the best solution and escape from the worst solution. Analyses of structures are performed by a finite element code in MATLAB. The effectiveness of JA algorithm is demonstrated through benchmark spatial truss 39-bar, and compare with results in references.

Źródło:

Quality Production Improvement - QPI; 2019, 1, 1; 238-243
2657-8603

Pojawia się w:

Quality Production Improvement - QPI

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 68.

Tytuł:

Comprehensive analysis of reclamation of spent lubricating oil using green solvent: RSM and ANN approach

Autorzy:

Sarkar, Sayantan
Datta, Deepshikha
Chowdhury, Somnath
Das, Bimal

Powiązania:

https://bibliotekanauki.pl/articles/2173421.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

modelling
optimization
extraction-flocculation
artificial neural network
genetic algorithm
modelowanie
optymalizacja
sztuczna sieć neuronowa
algorytm genetyczny

Opis:

Waste lubricating oil (WLO) is the most significant liquid hazardous waste, and indiscriminate disposal of waste lubricating oil creates a high risk to the environment and ecology. Present investigation emphasizes the re-refining of used automobile engine oil using the extraction-flocculation approach to reduce environmental hazards and convert the waste to energy. The extraction-flocculation process was modeled and optimized using response surface methodology (RSM), artificial neural network (ANN), and genetic algorithm (GA). The present study assessed parametric effects of refining time, refining temperature, solvent to waste oil ratio, and flocculant dosage. Experimental findings showed that the percentage of yield of recovered oil is to the tune of 86.13%. With the Central Composite Design approach, the maximum percentage of extracted oil is 85.95%, evaluated with 80 minutes of refining time, 50.17 C refining temperature, 7:1 solvent to waste oil ratio and flocculant dosage of 3 g/kg of solvent and 86.71% with 79.97 minutes refining time, 55.53 C refining temperature, 4.89:1 g/g solvent to waste oil ratio, 2.99 g/kg of flocculant concentration with Artificial Neural Network. A comparison shows that the ANN gives better results than the CCD approach. Physico-chemical properties of the recovered lube oil are comparable with the properties of fresh lubricating oil.

Źródło:

Chemical and Process Engineering; 2022, 43, 2; 119--135
0208-6425
2300-1925

Pojawia się w:

Chemical and Process Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 69.

Tytuł:

Correlational parameter tuning by genetic meta-algorithm

Autorzy:

Kieś, P.
Kosiński, W.

Powiązania:

https://bibliotekanauki.pl/articles/206578.pdf

Data publikacji:

2000

Wydawca:

Polska Akademia Nauk. Instytut Badań Systemowych PAN

Tematy:

adaptacja
algorytm genetyczny
optymalizacja
permutacja kodowa
strojenie parametrów
adaptation
code permutation
genetic algorithm
optimization
parameter tuning

Opis:

The general problem of an off-line parameter tuning in the Binary Genetic Algorithm (BGA) is introduced. An example of such a tuning: a class of Correlational Tuning Methods (CTMs) is proposed. The main idea of a CTM is that it uses a mapping called measurement function as an assessment of the BGA's effciency. An example of a measurement function is described and two examples of CTMs: a modified "trials and errors" method and a modified genetic meta-algoritlm (metaBGA) are shown. Finally, experimental results with the metaBGA for four kinds of test fitness functions, where the code permutation is the tuned parameter, are presented.

Źródło:

Control and Cybernetics; 2000, 29, 4; 1031-1042
0324-8569

Pojawia się w:

Control and Cybernetics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 70.

Tytuł:

Comparative Study of Optimised Artificial Intelligence Based First Order Sliding Mode Controllers for Position Control of a DC Motor Actuator

Autorzy:

Nyong-Bassey, B. E.
Akinloye, B.

Powiązania:

https://bibliotekanauki.pl/articles/385114.pdf

Data publikacji:

2016

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

adaptive fuzzy control
DC motor position control
genetic algorithm
particle swarm optimization (PSO)
sliding mode control

Opis:

This paper aims at critically reviewing various sliding mode control measures applied to Permanent Magnet DC Motor actuator for position control. At first, a hybrid sliding mode controller was examined with its advantages and disadvantages. Then, the smooth sliding mode controller in the same manner. The shortcomings of the two methods were overcome by proper switch design and also using tanh-sinh hyperbolic function. The sliding mode controller switches on when either disturbance or noise is detected. Genetic Algorithm Computational tuning technique is employed to optimize the gains of the controllers for optimal response.The performance of the proposed controller architecture, as well as the reviewed controllers, have been compared for performance evaluation with respect to several operating conditions. This includes load torque disturbance injection, noise injection in a feedback loop, motor nonlinearity exhibited by parameters variation, and a step change in reference input demand.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 3; 58-71
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 71.

Tytuł:

Multi-Objective Optimization of Squeeze Casting Process using Genetic Algorithm and Particle Swarm Optimization

Autorzy:

Patel, G. C. M.
Krishna, P.
Vundavilli, P. R.
Parappagoudar, M. B.

Powiązania:

https://bibliotekanauki.pl/articles/379601.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

squeeze casting process
multi-objective optimization
genetic algorithm
squeeze casting
prasowanie stopu
optymalizacja wielokryterialna
algorytm genetyczny

Opis:

The near net shaped manufacturing ability of squeeze casting process requiresto set the process variable combinations at their optimal levels to obtain both aesthetic appearance and internal soundness of the cast parts. The aesthetic and internal soundness of cast parts deal with surface roughness and tensile strength those can readily put the part in service without the requirement of costly secondary manufacturing processes (like polishing, shot blasting, plating, hear treatment etc.). It is difficult to determine the levels of the process variable (that is, pressure duration, squeeze pressure, pouring temperature and die temperature) combinations for extreme values of the responses (that is, surface roughness, yield strength and ultimate tensile strength) due to conflicting requirements. In the present manuscript, three population based search and optimization methods, namely genetic algorithm (GA), particle swarm optimization (PSO) and multi-objective particle swarm optimization based on crowding distance (MOPSO-CD) methods have been used to optimize multiple outputs simultaneously. Further, validation test has been conducted for the optimal casting conditions suggested by GA, PSO and MOPSO-CD. The results showed that PSO outperformed GA with regard to computation time.

Źródło:

Archives of Foundry Engineering; 2016, 16, 3; 172-186
1897-3310
2299-2944

Pojawia się w:

Archives of Foundry Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 72.

Tytuł:

A hybrid approach for scheduling transportation networks

Autorzy:

Dridi, M.
Kacem, I.

Powiązania:

https://bibliotekanauki.pl/articles/907640.pdf

Data publikacji:

2004

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

system transportowy
regulacja ruchu
algorytm genetyczny
optymalizacja wielokryterialna
transportation systems
traffic regulation
genetic algorithm
multicriteria optimization

Opis:

In this paper, we consider a regulation problem of an urban transportation network. From a given timetable, we aim to find a new schedule of multiple vehicles after the detection of a disturbance at a given time. The main objective is to find a solution maximizing the level of service for all passengers. This problem was intensively studied with evolutionary approaches and multi-agent techniques, but without identifying its type before. In this paper, we formulate the problem as a classical one in the case of an unlimited vehicle capacity. In the case of a limited capacity and an integrity constraint, the problem becomes difficult to solve. Then, a new coding and well-adapted operators are proposed for such a problem and integrated in a new evolutionary approach.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2004, 14, 3; 397-409
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 73.

Tytuł:

Zastosowanie metod programowania genetycznego w procesie maksymalizacji wydobycia węglowodorów przy zastosowaniu symulatora złożowego
Application of Genetic Programming Methods for the Optimization of Hydrocarbon Production by using a Reservoir Simulator

Autorzy:

Łętkowski, P.

Powiązania:

https://bibliotekanauki.pl/articles/1835296.pdf

Data publikacji:

2017

Wydawca:

Instytut Nafty i Gazu - Państwowy Instytut Badawczy

Tematy:

algorytmy genetyczne
programowanie genetyczne
optymalizacja wydobycia
symulacje złożowe
genetic algorithm
geneting programming
production optimization
filed simulations

Opis:

Artykuł poświęcono zastosowaniu metody programowania genetycznego dla celów optymalizacji wydobycia ropy naftowej na przykładzie testowego złoża węglowodorowego. Prezentowane zagadnienie optymalizacyjne jest prostym przykładem problemu optymalnej kontroli i polega na doborze wydajności wydobycia ropy naftowej w przyjętych przedziałach czasowych w taki sposób, aby w zadanym całkowitym czasie eksploatacji uzyskać maksymalne wydobycie sumaryczne przy minimalnym wydobyciu wody. Problem rozwiązano przy zastosowaniu algorytmu genetycznego, kodującego dozwolone wartości wydajności wydobycia z listy wartości dozwolonych. Z jednej strony działanie takie jest charakterystyczne dla metod programowania genetycznego, zaś z drugiej redukuje istotnie przestrzeń rozwiązań. W artykule zastosowano algorytm genetyczny Hollanda, dla którego zaimplementowano krzyżowanie wielopunktowe oraz adaptację prawdopodobieństw krzyżowania i mutacji na podstawie tzw. współczynnika zróżnicowania populacji. Działanie tak zdefiniowanego mechanizmu adaptacji jest następujące: jeżeli zróżnicowanie populacji rośnie, liniowo zwiększane jest prawdopodobieństwo krzyżowania, a zmniejszane prawdopodobieństwo mutacji; w przeciwnym wypadku (zróżnicowanie populacji maleje) działa mechanizm odwrotny, tzn. zmniejsza się prawdopodobieństwo krzyżowania, a zwiększa prawdopodobieństwo mutacji. Taka metoda z jednej strony gwarantuje różnorodność populacji, z drugiej zaś zapewnia dobrą eksploatację przestrzeni rozwiązań. Przeprowadzono szereg testów mających na celu zweryfikowanie efektywności algorytmu w zależności od liczby punktów krzyżowania (krzyżowanie 1-, 2-, 3-punktowe) oraz długości chromosomu. Wykonane testy wskazują na zadowalającą zbieżność algorytmu, niezależnie od wartości badanych parametrów. Przyjęcie funkcji w określonej postaci spowodowało premiowanie przez algorytm niższych wartości wydobycia, co wynika z nieliniowego przyrostu wydobycia wody dla wyższych wartości wydobycia ropy naftowej.
The paper addresses the problem of oil production optimization by genetic programming methods. The specific example of the problem presented in the paper belongs to the class of, so called, optimal control problems. It consists in finding the time variable rates of oil production that result in the maximum of the total oil production while keeping the total water production at a minimum available level. The problem is solved by a genetic algorithm, that assumes the production rates from the list of the allowable values. This approach typical for genetic programming methods significantly reduces the space of possible solutions. The article uses the Holland genetic algorithm for which multi-point crossing has been implemented and the adaptation of crossing and mutation probabilities based on so the called coefficient of population variability. The adaptive mechanism makes the crossing probability increase and mutation probability decrease for population variability increasing with time, while the crossing probability decrease and mutation probability increase for the variability decreasing with time. This mechanism guarantees the population variability to be at on appropriate level and at the same time, the extrapolation process for the solution space to be effective. Several tests were performed to verify the actual effectiveness of the algorithm for various number of crossing points (1, 2, 3 – crossing points) and chromosome length. Their results show a satisfactory convergence of the method to the final solution independent of the varying parameters values. Adopting a function in a specific form resulted in an algorithm for lower mining values, resulting from a nonlinear increase in water extraction for higher oil production values.

Źródło:

Nafta-Gaz; 2017, 73, 10; 760-767
0867-8871

Pojawia się w:

Nafta-Gaz

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 74.

Tytuł:

Multi-Objective Optimization of Motor Vessel Route

Autorzy:

Marie, S.
Courteille, E.

Powiązania:

https://bibliotekanauki.pl/articles/117604.pdf

Data publikacji:

2009

Wydawca:

Uniwersytet Morski w Gdyni. Wydział Nawigacyjny

Tematy:

route planning
Optimization of Vessel Route
multi-objective optimization
Motor Vessel
Optimal Route
Multi-Objective Genetic Algorithm (MOGA)
Bézier Curve
MATLAB

Opis:

This paper presents an original method that allows computation of the optimal route of a motor vessel by minimizing its fuel consumption. The proposed method is based on a new and efficient meshing procedure that is used to define a set of possible routes. A consumption prediction tool has been developed in order to estimate the fuel consumption along a given trajectory. The consumption model involves the effects of the meteorological conditions, the shape of the hull and the power train characteristics. Pareto-optimization with a Multi-Objective Genetic Algorithm (MOGA) is taken as a framework for the definition and the solution of the multi-objective optimization problem addressed. The final goal of this study is to provide a decision helping tool giving the route that minimizes the fuel consumption in a limited or optimum time.

Źródło:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2009, 3, 2; 133-141
2083-6473
2083-6481

Pojawia się w:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 75.

Tytuł:

Multi-parametric and multi-objective thermodynamic optimization of a spark-ignition range extender ICE

Autorzy:

Toman, R.
Brankov, I.

Powiązania:

https://bibliotekanauki.pl/articles/243112.pdf

Data publikacji:

2018

Wydawca:

Instytut Techniczny Wojsk Lotniczych

Tematy:

Range Extender
hybrid electric vehicle
battery electric vehicle
internal combustion engine
spark ignition
thermodynamic optimization
genetic algorithm

Opis:

The current legislation pushes for the increasing level of vehicle powertrain electrification. A series hybrid electric vehicle powertrain with a small Range Extender (REx) unit – comprised of an internal combustion engine and an electric generator – has the technical potential to overcome the main limitations of a pure battery electric vehicle: driving range, heating, and air-conditioning demands. A typical REx ICE operates only in one or few steady-states operating points, leading to different initial priorities for its design. These design priorities, compared to the conventional ICE, are mainly NVH, package, weight, and overall concept functional simplicity – hence the costeffectiveness. The design approach of the OEMs is usually rather conservative: parting from an already-existing ICE or components and adapting it for the REx application. The fuel efficiency potential of a one-point operation of the REx ICE is therefore not fully exploited. This article presents a multi-parametric and multi-objective optimization study of a REx ICE. The studied ICE concept uses a well-known and proven technology with a favourable production and development costs: it is a two-cylinder, natural aspirated, port injected, four-stroke SI engine. The goal of our study is to find its thermodynamic optimum and fuel efficiency potential for different feasible brake power outputs. Our optimization tool-chain combines a parametric GT-Suite ICE simulation model and modeFRONTIER optimization software with various optimization strategies, such as genetic algorithms, gradient based methods or various hybrid methods. The optimization results show a great fuel efficiency improvement potential by applying this multi-parametric and multi-objective method, converging to interesting short-stroke designs with Miller valve timings.

Źródło:

Journal of KONES; 2018, 25, 3; 459-466
1231-4005
2354-0133

Pojawia się w:

Journal of KONES

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 76.

Tytuł:

Noise Elimination of Reciprocating Compressors Using FEM, Neural Networks Method, and the GA Method

Autorzy:

Chang, Y.-C.
Chiu, M.-C.
Xie, J.-L.

Powiązania:

https://bibliotekanauki.pl/articles/178126.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

finite element method
polynomial neural network model
genetic algorithm
group method of data handling
reciprocating compressor
optimization

Opis:

Industry often utilizes acoustical hoods to block noise emitted from reciprocating compressors. However, the hoods are large and bulky. Therefore, to diminish the size of the compressor, a compact discharge muffler linked to the compressor outlet is considered. Because the geometry of a reciprocating compressor is irregular, COMSOL, a finite element analysis software, is adopted. In order to explore the acoustical performance, a mathematical model is established using a finite element method via the COMSOL commercialized package. Additionally, to facilitate the shape optimization of the muffler, a polynomial neural network model is adopted to serve as an objective function; also, a Genetic Algorithm (GA) is linked to the OBJ function. During the optimization, various noise abatement strategies such as a reverse expansion chamber at the outlet of the discharge muffler and an inner extended tube inside the discharge muffler, will be assessed by using the artificial neural network in conjunction with the GA optimizer. Consequently, the discharge muffler that is optimally shaped will decrease the noise of the reciprocating compressor.

Źródło:

Archives of Acoustics; 2017, 42, 2; 189-197
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 77.

Tytuł:

Decentralized PID control by using GA optimization applied to a quadrotor

Autorzy:

Hasseni, S. E. I.
Abdou, L.

Powiązania:

https://bibliotekanauki.pl/articles/384905.pdf

Data publikacji:

2018

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

quadrotor
non-linear systems
decentralized control
PID
optimization
genetic algorithm
systemy nieliniowe
kontrola zdecentralizowana
optymalizacja
algorytm genetyczny

Opis:

Quadrotors represent an effective class of aerial robots because of their abilities to work in small areas. We suggested in this research paper to develop an algorithm to control a quadrotor, which is a nonlinear MIMO system and strongly coupled, by a linear control technique (PID), while the parameters are tuned by the Genetic Algorithm (GA). The suggested technique allows a decentralized control by decoupling the linked interactions to effect angles on both altitude and translation position. Moreover, the using a meta-heuristic technique enables a certain ability of the system controllers design without being limited by working on just the small angles and stabilizing just the full actuated subsystem. The simulations were implemented in MATLAB/Simulink tool to evaluate the control technique in terms of dynamic performance and stability. Although the controllers design (PID) is simple, it shows the effect of the proposed technique in terms of tracking errors and stability, even with large angles, subsequently, high velocity response and high dynamic performances with practically acceptable rotors speed.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2018, 12, 2; 33-44
1897-8649
2080-2145

Pojawia się w:

Journal of Automation Mobile Robotics and Intelligent Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 78.

Tytuł:

Probabilistic model-building algorithms as tool to find optimum of a function
Algorytmy z modelem probabilistycznym jako narzędzie optymalizacji funkcji

Autorzy:

Reichel, A.
Nowak, I.

Powiązania:

https://bibliotekanauki.pl/articles/87296.pdf

Data publikacji:

2015

Wydawca:

Politechnika Śląska. Wydawnictwo Politechniki Śląskiej

Tematy:

algorytm PBIL
algorytm cGA
metody heurystyczne
optymalizacja
population-based incremental learning
compact genetic algorithm
heuristic methods
optimization

Opis:

The aim of this paper is to present the probabilistic modelbuilding heuristics which is a modification of an evolutionary algorithm. the Probabilistic-Based Incremental Learning (PBIL) and the compact Genetic Algorithm (cGA) is presented as a example of the probabilistic model building algorithms dedicated to the binary problems. Both heuristics are tested on three functions that allow to investigate the advantages, disadvantages and limitations of methods under consideration.
Celem niniejszego artykułu jest przedstawienie heurystyk wieloagentowych wykorzystujących model probabilistyczny. W artykule omówiono dwie metody: the Probabilistic-Based Incremental Learning (PBIL) oraz the compact Genetic Algorithm (cGA), będące przykładami heurystyk z modelem probabilistycznym. Obie metody są przeznaczone do rozwiązywania problemów binarnych. W ramach pracy metody te testowano na trzech funkcjach zdefiniowanych w przestrzeni ciągów binarnych. Testy miały zbadać zalety, wady oraz ograniczenia obu prezentowanych heurystyk populacyjnych.

Źródło:

Zeszyty Naukowe. Matematyka Stosowana / Politechnika Śląska; 2015, 5; 79-97
2084-073X

Pojawia się w:

Zeszyty Naukowe. Matematyka Stosowana / Politechnika Śląska

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 79.

Tytuł:

Optimization of process parameters in turning of magnesium AZ91D alloy for better surface finish using genetic algorithm

Autorzy:

Pradeep Kumar, Madhesan
Venkatesan, Rajamanickam
Manimurugan, Manickam

Powiązania:

https://bibliotekanauki.pl/articles/2142864.pdf

Data publikacji:

2022

Wydawca:

Centrum Badań i Innowacji Pro-Akademia

Tematy:

genetic algorithm
magnesium alloy
turning
optimization
Pareto front
RSM
algorytm genetyczny
stopy magnezu
obracanie
optymalizacja
front Pareto

Opis:

This research examined at the optimum cutting parameters for producing minimum surface roughness and maximum Material Removal Rate (MRR) when turning magnesium alloy AZ91D. Cutting speed (m/min), feed (mm/rev), and cut depth (mm) have all been considered in the experimental study. To find the best cutting parameters, Taguchi's technique and Response Surface Methodology (RSM), an evolutionary optimization techniques Genetic Algorithm (GA) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) were employed. GA gives better results of 34.04% lesser surface roughness and 15.2% higher MRR values when compared with Taguchi method. The most optimal values of surface roughness and MRR is received in multi objective optimization NSGA-II were 0.7341 µm and 9460 mm3/min for the cutting parameters cutting speed at 140.73m/min, feed rate at 0.06mm/min and 0.99mm depth of cut. Multi objective NSGA-II optimization provides several non-dominated points on Pareto Front model that can be utilized as decision making for choice among objectives.

Źródło:

Acta Innovations; 2022, 43; 54-62
2300-5599

Pojawia się w:

Acta Innovations

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 80.

Tytuł:

An adaptive particle swarm optimization algorithm for robust trajectory tracking of a class of under actuated system

Autorzy:

Kumar, V. E.
Jerome, J.

Powiązania:

https://bibliotekanauki.pl/articles/141105.pdf

Data publikacji:

2014

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

inverted pendulum
LQR controller
particle swarm optimization (PSO)
genetic algorithm
adaptive inertia weight factor
state feedback control

Opis:

This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.

Źródło:

Archives of Electrical Engineering; 2014, 63, 3; 345-365
1427-4221
2300-2506

Pojawia się w:

Archives of Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 81.

Tytuł:

Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm

Autorzy:

El-Samak, A. F.
Ashour, W.

Powiązania:

https://bibliotekanauki.pl/articles/91810.pdf

Data publikacji:

2015

Wydawca:

Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych

Tematy:

combinatorial optimization problem
travel salesman problem
genetic algorithm
evolutionary computation algorithm
affinity propagation clustering technique
AP
problem optymalizacji kombinatorycznej
algorytm genetyczny
obliczenia ewolucyjne

Opis:

Combinatorial optimization problems, such as travel salesman problem, are usually NPhard and the solution space of this problem is very large. Therefore the set of feasible solutions cannot be evaluated one by one. The simple genetic algorithm is one of the most used evolutionary computation algorithms, that give a good solution for TSP, however, it takes much computational time. In this paper, Affinity Propagation Clustering Technique (AP) is used to optimize the performance of the Genetic Algorithm (GA) for solving TSP. The core idea, which is clustering cities into smaller clusters and solving each cluster using GA separately, thus the access to the optimal solution will be in less computational time. Numerical experiments show that the proposed algorithm can give a good results for TSP problem more than the simple GA.

Źródło:

Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 4; 239-245
2083-2567
2449-6499

Pojawia się w:

Journal of Artificial Intelligence and Soft Computing Research

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 82.

Tytuł:

Numeryczna optymalizacja profili śmigłowcowych oparta na algorytmie genetycznym z uwzględnieniem kryteriów bazujących na niestacjonarnych charakterystykach aerodynamicznych
Numerical optimization rotorcraft blade airfoils based on genetic algorithm and unstedy aerodynamic criteria

Autorzy:

Stalewski, W.

Powiązania:

https://bibliotekanauki.pl/articles/212749.pdf

Data publikacji:

2006

Wydawca:

Sieć Badawcza Łukasiewicz - Instytut Lotnictwa

Tematy:

numeryczna optymalizacja profili śmigłowców
algorytm genetyczny
niestacjonarne charakterystyki aerodynamiczne
numerical optimization of rotorcraft blade
genetic algorithm
areodynamic criteria

Opis:

W pracy przedstawiono metodę numerycznej optymalizacji profili lotniczych, ze szczególnym uwzględnieniem profili łopat wirnika nośnego śmigłowca. Metoda bazuje na algorytmie genetycznym. Optymalizacja realizowana jest poprzez symulację procesów ewolucji gatunków regulowanych prawami doboru naturalnego. Wielokryterialny algorytm genetyczny adaptowano na przypadek optymalizacji profili lotniczych. Opracowano wyspecjalizowany generator kształtu profili a także metodę analizy niestacjonarnego, transonicznego opływu profilu z uwzględnieniem silnego oddziaływania niestacjonarnej warstwy przyściennej. W oparciu o opracowane moduły obliczeniowe powstał pakiet oprogramowania OGA pozwalający rozwiązywać szeroką klasę zagadnień z zakresu wielokryterialnej optymalizacji profili śmigłowcowych. Przedstawiono wyniki obliczeń testowych wykonanych za pomocą opracowanego pakietu.
The optimization methodology of rotorcraft airfoils was presented. The methodology is based on the multi-objectiye genetic algorithm, where the process of optimization imitates the real processes of natural selection and heredity observed in lively nature. Basing on the worked out methodology the optimization software package was developed. This package enables a computational design of airfoils, which have the best aerodynamic properties with regard to given criteria. The multi-objective optimization with additional aerodynamic and geometrie constraints was applied. The specialized airfoil shape parameterization was worked out to get the wide class of airfoil geometry. The optimization criteria are directed to reąuirements and flow conditions specific to rotorcraft airfoils. In particular, the criteria are evaluated basing on unsteady aerodynamic characteristics that correspond to unsteady flow around rotor blades of rotorcraft in flight. The method of calculation of the unsteady, transonic flow with unsteady boundary layer analysis was developed. The method is used to evaluate both unsteady and steady aerodynamic objectives and constraints. Additionally, the several external codes were included inthe package, to get the wide possibilities of steady aerodynamic criteria formulation. The thorough verification of presented optimization software package was performed. The results confirmed the proper work of the package as well as its wide possibilities of use as the tool supporting the aerodynamic design.

Źródło:

Prace Instytutu Lotnictwa; 2006, 1-2 (184-185); 54-64
0509-6669
2300-5408

Pojawia się w:

Prace Instytutu Lotnictwa

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 83.

Tytuł:

Optimization with the use of genetic algorithms of the location depth of horizontal ground heat exchangers
Optymalizacja za pomocą algorytmów genetycznych głębokości położenia poziomych gruntowych wymienników ciepła

Autorzy:

Neugebauer, M.
Sołowiej, P.

Powiązania:

https://bibliotekanauki.pl/articles/286313.pdf

Data publikacji:

2012

Wydawca:

Polskie Towarzystwo Inżynierii Rolniczej

Tematy:

algorytmy genetyczne
optymalizacja
poziomy gruntowy wymiennik ciepła
pompa ciepła
genetic algorithm
optimization
horizontal ground heat exchanger
heat pump

Opis:

The objective of the paper was to prepare a method describing a minimum depth for installation of a ground heat exchanger in the heat pumps systems. The lower is the location depth of the horizontal ground heat exchangers (HGHE), the lower are the geothermal in-stallation costs with the horizontal ground heat exchanger and consequently the higher cost-effectiveness of an investment. A task of two-criteria optimization was formulated - i.e. de-termination of the minimum depth and a diameter of the HGHE pipes assuming a particular temperature of the operational factor on the output from the HGHE. Reduction of the depth of the HGHE translates into the decrease of installation costs. The module of genetic algorithms from MatLab application was used to carry out optimization. Within the calculations, which were carried out, validity of optimization of the installation depth of HGHE and no reason for optimization of the pipes diameter were confirmed.
Celem pracy było opracowanie metody określenia minimalnej głębokości zainstalowania gruntowego, poziomego wymiennika ciepła w układach pomp ciepła. Im mniejsza głębokość usytuowania PGWC, tym niższe koszty instalacji geotermicznej z poziomym gruntowym wymien-nikiem ciepła, a co za tym idzie większa opłacalność inwestycji. Sformułowano zadanie optymalizacji dwukryterialnej - określenie minimalnej głębokości i średnicy rur PGWC przy założeniu określonej temperatury czynnika roboczego na wyjściu z PGWC. Zmniejszenie głębokości PGWC przekłada się na zmniejszenie kosztów wykonania instalacji. Do przeprowadzenia optymalizacji wykorzystano moduł algorytmów genetycznych z programu MatLab. W ramach przeprowadzonych obliczeń potwierdzono zasadność optymalizacji głębokości instalacji PGWC i brak sensowności optymalizacji średnicy rur.

Źródło:

Inżynieria Rolnicza; 2012, R. 16, nr 4, t. 2, 4, t. 2; 89-97
1429-7264

Pojawia się w:

Inżynieria Rolnicza

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 84.

Tytuł:

Аналіз теоретичних основ оптимізації портфеля високих технологій
The analysis of theoretical foundations of optimization a high technology room

Autorzy:

Omelianenko, V.A.

Powiązania:

https://bibliotekanauki.pl/articles/692374.pdf

Data publikacji:

2014

Wydawca:

Dnieprowski Uniwersytet Narodowy im. Ołesia Honczara

Tematy:

технологічний портфель
генетичний алгоритм
оптимізація
високі технології
трансфер технологій
technology portfolio
genetic algorithm
optimization
high technology
technology transfer

Opis:

The paper deals with the characteristics of technological portfolio of high-tech enterprises. The main approaches to determine the composition of the portfolio process and the main factors of its formation were analyzed and systematized. The possibility of using genetic algorithms to control the technological portfolio were Identified and offered the theoretical foundations of the optimization of its structure on the basis of resource and innovation criteria
Проаналізовано особливості управління технологічним портфелем високотехнологічних підприємств. Проаналізовано та систематизовано основні підходи до визначення складу технологічного портфеля та основні фактори його формування. Визначено можливості використання генетичних алгоритмів для управління технологічним портфелем та запропоновано теоретичні основи оптимізації його складу на основі ресурсних та інноваційних критеріїв.

Źródło:

European Journal of Management Issues; 2014, 3; 53-61
2519-8564

Pojawia się w:

European Journal of Management Issues

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 85.

Tytuł:

Ligament-based spine-segment mechanisms

Autorzy:

Ciszkiewicz, A.
Milewski, G.

Powiązania:

https://bibliotekanauki.pl/articles/202123.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

intervertebral joint
optimization
parameters estimation
genetic algorithm
elastostatic analysis
staw międzykręgowy
optymalizacja
szacowanie parametrów
algorytm genetyczny
analiza elastostatyczna

Opis:

Nowadays, a growing interest in spine-segment mechanisms for humanoid robots can be observed. The ones currently available are mostly inspired by an intervertebral joint but rarely use its structure and behaviour as input data. The aim of this study was to propose and verify an approach to spine-segment mechanisms synthesis, in which the mechanisms were obtained directly from a ligament system of the intervertebral joint through numerical optimization. The approach consists of two independent optimization procedures performed with genetic algorithm. The first one searches for the optimal structure, while the second estimates its geometrical and stiffness parameters. The mechanisms are rated by their ability to reproduce the static behaviour of the joint in selected aspects. Both procedures use the lumbar L4-L5 intervertebral joint reference data. The approach was tested in two numerical scenarios. It was possible to obtain a mechanism with 7 flexible linear legs that accurately emulated the elastostatic behaviour of the intervertebral joint under moment loads. The results prove that the proposed method is feasible and worth exploring. It may be employed in design of bioinspired joints for use in humanoid robots and can also serve as an initial step in the design of prosthetic and orthotic devices for a human spine.

Źródło:

Bulletin of the Polish Academy of Sciences. Technical Sciences; 2018, 66, 5; 705-712
0239-7528

Pojawia się w:

Bulletin of the Polish Academy of Sciences. Technical Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 86.

Tytuł:

Optimization of compressed heat exchanger efficiency by using genetic algorithm

Autorzy:

Ghorbani, M.
Ranjbar, S. F.

Powiązania:

https://bibliotekanauki.pl/articles/266267.pdf

Data publikacji:

2019

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

wymiennik ciepła
spadek ciśnienia
pojemność cieplna
optymalizacja
algorytm genetyczny
heat exchanger
pressure drop
heat capacity
optimization
genetic algorithm

Opis:

Due to the application of coil-shaped coils in a compressed gas flow exchanger and water pipe flow in airconditioner devices, air conditioning and refrigeration systems, both industrial and domestic, need to be optimized to improve exchange capacity of heat exchangers by reducing the pressure drop. Today, due to the reduction of fossil fuel resources and the importance of optimal use of resources, optimization of thermal, mechanical and electrical devices has gained particular importance. Compressed heat exchangers are the devices used in industries, especially oil and petrochemical ones, as well as in power plants. So, in this paper we try to optimize compressed heat exchangers. Variables of the functions or state-of-the-machine parameters are optimized in compressed heat exchangers to achieve maximum thermal efficiency. To do this, it is necessary to provide equations and functions of the compressed heat exchanger relative to the functional variables and then to formulate the parameter for the gas pressure drop of the gas flow through the blades and the heat exchange surface in relation to the heat duty. The heat transfer rate to the gas-side pressure drop is maximized by solving the binary equation system in the genetic algorithm. The results show that using optimization, the heat capacity and the efficiency of the heat exchanger improved by 15% and the pressure drop along the path significantly decreases.

Źródło:

International Journal of Applied Mechanics and Engineering; 2019, 24, 2; 461-472
1734-4492
2353-9003

Pojawia się w:

International Journal of Applied Mechanics and Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 87.

Tytuł:

Generalized route planning approach for hazardous materials transportation with equity consideration

Autorzy:

Chai, H.
He, R.-C.
Jia, X.-yan
Ma, Ch.-x
Dai, C.-jie

Powiązania:

https://bibliotekanauki.pl/articles/223759.pdf

Data publikacji:

2018

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

hazardous materials transportation
transportation
route optimization
risk equity
multi-objective optimization
NSGA-II algorithm
genetic algorithm
transport materiałów niebezpiecznych
materiały niebezpieczne
optymalizacja trasy
kapitał własny
optymalizacja wielokryterialna
algorytm NSGA-II
algorytm genetyczny

Opis:

Hazardous materials transportation should consider risk equity and transportation risk and cost. In the hazardous materials transportation process, we consider risk equity as an important condition in optimizing vehicle routing for the long-term transport of hazardous materials between single or multiple origin-destination pairs (O-D) to reduce the distribution difference of hazardous materials transportation risk over populated areas. First, a risk equity evaluation scheme is proposed to reflect the risk difference among the areas. The evaluation scheme uses standard deviation to measure the risk differences among populated areas. Second, a risk distribution equity model is proposed to decrease the risk difference among populated areas by adjusting the path frequency between O-D pairs for hazardous materials transportation. The model is converted into two sub models to facilitate decision-making, and an algorithm is provided for each sub model. Finally, we design a numerical example to verify the accuracy and rationality of the model and algorithm. The numerical example shows that the proposed model is essential and feasible for reducing the complexity and increasing the portability of the transportation process.

Źródło:

Archives of Transport; 2018, 46, 2; 33-46
0866-9546
2300-8830

Pojawia się w:

Archives of Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 88.

Tytuł:

The optmization tool supporting supply chain management in the multi-criteria approach
Optymalizacyjne narzędzie wspomagające zarządzanie łańcuchem dostaw w ujęciu wielokryterialnym

Autorzy:

Izdebski, M.
Jacyna-Gołda, I.
Gołębiowski, P.
Plandor, J.

Powiązania:

https://bibliotekanauki.pl/articles/962267.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

optymalizacja wielokryterialna
algorytm genetyczny
infrastruktura transportowa
zarządzanie łańcuchem dostaw
multi-criteria optimization
genetic algorithm
transport infrastructure
supply chain management

Opis:

W artykule przedstawiono nowe narzędzie optymalizacyjne wspierające zarządzanie łańcuchem dostaw w aspekcie wielokryterialnym. To narzędzie zostało wdrożone w systemie EPLOS (Europejski Portal Usług Logistycznych). System EPLOS to zintegrowany system informatyczny wspierający proces tworzenia sieci dostaw i dystrybucji w łańcuchach dostaw. Ten system składa się z wielu modułów, np. moduł optymalizacji odpowiedzialny za przetwarzanie danych, generowanie wyników, moduł danych wejściowych, moduł kalibracji parametrów algorytmu optymalizacyjnego. Głównym celem badań było opracowanie systemu do określania parametrów łańcucha dostaw, które wpływają na jego efektywność w procesie zarządzania przepływem towarów między poszczególnymi ogniwami łańcucha. Parametry te zostały uwzględnione w modelu matematycznym jako zmienne decyzyjne w celu ustalenia ich w procesie optymalizacji. W modelu matematycznym zdefiniowano dane wejściowe adekwatne do analizowanego problemu, przedstawiono główne ograniczenia związane z wyznaczaniem efektywnego sposobu zarządzania łańcuchem dostaw oraz opisano funkcje kryterium. Problem zarządzania przepływem towarów w łańcuchu dostaw został przedstawiony w ujęciu wielokryterialnym. Ocenę efektywności zarządzania łańcuchem dostaw przeprowadzono na podstawie globalnej funkcji kryterium składającej się z częściowych funkcji kryteriów opisanych w modelu matematycznym. Główne funkcje kryteriów na podstawie których wyznaczane jest końcowe rozwiązane to współczynnik wykorzystania wewnętrznych środków transportu, współczynnik wykorzystania zewnętrznych środków transportu, koszty pracy środków transportu wewnętrznego i personelu, całkowity koszt realizacji zadań transportowych, współczynnik wykorzystania czasu zaangażowania pojazdów, całkowity czas poświęcony na wykonanie zadań, czy liczba pojazdów. Punktem wyjścia do badania było założenie, że o skuteczności zarządzania łańcuchem decydują dwa problemy decyzyjne ważne dla menedżerów w procesie zarządzania łańcuchem dostaw, tj. problem przydziału pojazdów do zadań i problem lokalizacji obiektów logistycznych w łańcuchu dostaw. Aby rozwiązać badany problem, zaproponowano innowacyjne podejście w postaci opracowania algorytmu genetycznego, który został dostosowane do przedstawionego modelu matematycznego. W pracy szczegółowo opisano poszczególne kroki konstruowania algorytmu. Zaproponowana struktura przetwarzana przez algorytm jest strukturą macierzową, dzięki której wyznaczane są optymalne parametry łańcucha dostaw. Procesy krzyżowania i mutacji zostały opracowane adekwatnie do przyjętej struktury macierzowej. W procesie kalibracji algorytmu wyznaczono takie wartości parametrów algorytmu tj. prawdopodobieństwo krzyżowania czy mutacji, które generują optymalne rozwiązanie. Poprawność algorytmu genetycznego oraz efektywność zaproponowanego narzędzia wspomagającego proces zarządzania łańcuchem dostaw została potwierdzona w procesie jego weryfikacji.
The article presents a new optimization tool supporting supply chain management in the multi-criteria aspect. This tool was implemented in the EPLOS system (European Logistics Services Portal system). The EPLOS system is an integrated IT system supporting the process of creating a supply and distribution network in supply chains. This system consists of many modules e.g. optimization module which are responsible for data processing, generating results. The main objective of the research was to develop a system to determine the parameters of the supply chain, which affect its efficiency in the process of managing the goods flow between individual links in the chain. These parameters were taken into account in the mathematical model as decision variables in order to determine them in the optimization process. The assessment of supply chain management effectiveness was carried out on the basis of the global function of the criterion consisting of partial functions of the criteria described in the mathematical model. The starting point for the study was the assumption that the effectiveness of chain management is determined by two important decision-making problems that are important for managers in the supply chain management process, i.e. the problem of assigning vehicles to tasks and the problem of locating logistics facilities in the supply chain. In order to solve the problem, an innovative approach to the genetic algorithm was proposed, which was adapted to the developed mathematical model. The correctness of the genetic algorithm has been confirmed in the process of its verification.

Źródło:

Archives of Civil Engineering; 2020, 66, 3; 505-524
1230-2945

Pojawia się w:

Archives of Civil Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 89.

Tytuł:

Optymalizacja kratownicy z zastosowaniem algorytmu genetycznego
Optimization of truss using genetic algorithm

Autorzy:

Grzywiński, M.

Powiązania:

https://bibliotekanauki.pl/articles/2065529.pdf

Data publikacji:

2018

Wydawca:

Politechnika Częstochowska

Tematy:

algorytm genetyczny
optymalizacja przekroju
zmienne dyskretne
minimum masy
emisja CO2
genetic algorithm
size optimization
discrete variables
mass minimum

Opis:

W artykule zaproponowano algorytm genetyczny do rozwiązania problemu minimalizacji masy płaskiej kratownicy, biorąc pod uwagę zmienność pola przekroju. Minimalna masa konstrukcji stalowej to też niska emisja CO2. Konstrukcja jest zoptymalizowana za pomocą wydajnego algorytmu zwanego Teaching Learning Based Optimization. Proces TLBO jest podzielony na dwie części: pierwsza składa się z "fazy nauczyciela", a druga składa się z "fazy ucznia". Obliczenia wykonywane są z pomocą programu metody elementów skończonych zakodowanym w MATLAB-ie.
The article proposes a genetic algorithm for solving the problem of minimizing the mass of a plane truss, taking into account the variability of the cross-sectional area. The minimum mass of the steel structure is also low CO2 emission. The design is optimized using an efficient algorithm called Teaching Learning Based Optimization. The TLBO process is divided into two parts: the first consists of the "teacher phase" and the second consists of the "student phase". The calculations are performed with the help of the finite element method program coded in MATLAB.

Źródło:

Budownictwo o Zoptymalizowanym Potencjale Energetycznym; 2018, 7, 2; 117--122
2299-8535
2544-963X

Pojawia się w:

Budownictwo o Zoptymalizowanym Potencjale Energetycznym

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 90.

Tytuł:

The stress-minimizing hole in a shear-loaded elastic plate at a given energy increment

Autorzy:

Vigdergauz, S.

Powiązania:

https://bibliotekanauki.pl/articles/38694050.pdf

Data publikacji:

2022

Wydawca:

Instytut Podstawowych Problemów Techniki PAN

Tematy:

2D elastostatic problem
Kolosov–Muskhelishvili potentials
stress concentration
factor
shape optimization
effective energy
extremal elastic structures
genetic algorithm

Opis:

Minimization of the peak tangential stresses around a single hole in an infinite 2D elastic plate under remote pure shear and a given hole-induced strain energy level is considered as a free-shape optimization problem under a physical constraint. It is solved by combining a genetic algorithm with the almost analytical, and hence highly accurate stress-strain solver for any finitely parameterized family of closed curves. The results obtained in wide ranges of the governing parameters are detailed and discussed. They may be applicable to the optimal holes design in constructive elements and dilute perforated structures. The current analysis extends the author’s previous publications, which were focused on the unconstrained shape optimization within the same setup.

Źródło:

Archives of Mechanics; 2022, 74, 2-3; 109-126
0373-2029

Pojawia się w:

Archives of Mechanics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 91.

Tytuł:

Calculation strength optimum of surgical robot effector for mechanical eigenproblems using FEM and genetic algorithm

Autorzy:

Ilewicz, Grzegorz

Powiązania:

https://bibliotekanauki.pl/articles/24201993.pdf

Data publikacji:

2023

Wydawca:

Politechnika Poznańska. Instytut Mechaniki Stosowanej

Tematy:

surgical robot
resonance phenomena
elastic buckling
optimization
genetic algorithm
FEM
robot chirurgiczny
zjawisko rezonansu
wyboczenie sprężyste
optymalizacja
algorytm genetyczny
MES

Opis:

It is essential to check whether the surgical robot end effector is safe to use due to phenomena such as linear buckling and mechanical resonance. The aim of this research is to build an multi criteria optimization model based on such criteria as the first natural frequency, buckling factor and mass, with the assumption of the basic constraint in the form of a safety factor. The calculations are performed for a serial structure of surgical robot end effector with six degrees of freedom ended with a scalpel. The calculation model is obtained using the finite element method. The issue of multi-criteria optimization is solved based on the response surface method, Pareto fronts and the genetic algorithm. The results section illustrates deformations of a surgical robot end effector occurring during the resonance phenomenon and the buckling deformations for subsequent values of the buckling coefficients. The dependencies of the geometrical dimensions on the criteria are illustrated with the continuous functions of the response surface, i.e. metamodels. Pareto fronts are illustrated, based on which the genetic algorithm finds the optimal quantities of the vector function. The conducted analyzes provide a basis for selecting surgical robot end effector drive systems from the point of view of their generated inputs.

Źródło:

Vibrations in Physical Systems; 2023, 34, 1; art. no. 2023106
0860-6897

Pojawia się w:

Vibrations in Physical Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 92.

Tytuł:

Decentralized job scheduling in the cloud based on a spatially generalized Prisoner’s Dilemma game

Autorzy:

Gąsior, J.
Seredyński, F.

Powiązania:

https://bibliotekanauki.pl/articles/329736.pdf

Data publikacji:

2015

Wydawca:

Uniwersytet Zielonogórski. Oficyna Wydawnicza

Tematy:

job scheduling
multiobjective optimization
genetic algorithm
prisoner's dilemma
cellular automata
harmonogramowanie zadań
optymalizacja wielokryterialna
algorytm genetyczny
dylemat więźnia
automat komórkowy

Opis:

We present in this paper a novel distributed solution to a security-aware job scheduling problem in cloud computing infrastructures. We assume that the assignment of the available resources is governed exclusively by the specialized brokers assigned to individual users submitting their jobs to the system. The goal of this scheme is allocating a limited quantity of resources to a specific number of jobs minimizing their execution failure probability and total completion time. Our approach is based on the Pareto dominance relationship and implemented at an individual user level. To select the best scheduling strategies from the resulting Pareto frontiers and construct a global scheduling solution, we developed a decision-making mechanism based on the game-theoretic model of Spatial Prisoner’s Dilemma, realized by selfish agents operating in the two-dimensional cellular automata space. Their behavior is conditioned by the objectives of the various entities involved in the scheduling process and driven towards a Nash equilibrium solution by the employed social welfare criteria. The performance of the scheduler applied is verified by a number of numerical experiments. The related results show the effectiveness and scalability of the scheme in the presence of a large number of jobs and resources involved in the scheduling process.

Źródło:

International Journal of Applied Mathematics and Computer Science; 2015, 25, 4; 737-751
1641-876X
2083-8492

Pojawia się w:

International Journal of Applied Mathematics and Computer Science

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 93.

Tytuł:

Badanie i analiza algorytmów rojowych w optymalizacji parametrów regulatora kursu statku
Study and analysis of swarm intelligence in optimizing parameters of the ship course controller

Autorzy:

Tomera, M.

Powiązania:

https://bibliotekanauki.pl/articles/266857.pdf

Data publikacji:

2015

Wydawca:

Politechnika Gdańska. Wydział Elektrotechniki i Automatyki

Tematy:

algorytmy rojowe
algorytm genetyczny
optymalizacja stochastyczna
regulator PID
sterowanie statkiem
swarm intelligence
genetic algorithm
random optimization
PID controller
ship control

Opis:

W pracy przedstawione zostały badania i analiza zastosowania wybranych algorytmów rojowych do optymalizacji parametrów regulatora PID w układzie sterowania statkiem na kursie. Optymalizacja ta polegała na minimalizacji czasowego wskaźnika jakości wyznaczanego na podstawie odpowiedzi skokowej. Do optymalizacji parametrów regulatora kursu statku wykorzystane zostały algorytmy rojowe, takie jak: algorytm mrówkowy, zmodyfikowany algorytm mrówkowy, algorytm sztucznej kolonii pszczół oraz algorytm optymalizacji rojem cząstek. Przeprowadzone zostały badania szybkości znajdowania optymalnego rozwiązania i wykonana została analiza porównawcza uzyskanych wyników. Zaprezentowane wyniki badań pozwalają stwierdzić, że algorytm optymalizacji rojem cząstek charakteryzuje się najlepszą jakością optymalizacji parametrów regulatora kursu statku.
The paper presents the research and analysis of the use of certain swarm intelligence algorithms to optimize the parameters of PID control in a ship on the course. This optimization was to minimize the performance quality index based on step response of the mathematical model of control system. To optimize the parameters of the ship course controller have been used swarm intelligence algorithms, such as: ant colony algorithm (ACO), the modified ant colony algorithm (MACO), the artificial bee colony algorithm (ABC) and the particle swarm optimization algorithm (PSO). Rate tests were conducted to find the optimal solution and a comparative analysis of the results was made. The presented results of research allow us to conclude that the particle swarm optimization (PSO) algorithm has the best quality of optimizing the control parameters of the course controller.

Źródło:

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2015, 46; 103-106
1425-5766
2353-1290

Pojawia się w:

Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 94.

Tytuł:

Calculating extreme criteria of eigenvalue problems of a medical robot for soft tissue surgery using the multi-objective optimization

Autorzy:

Ilewicz, Grzegorz

Powiązania:

https://bibliotekanauki.pl/articles/2146592.pdf

Data publikacji:

2020

Wydawca:

Politechnika Poznańska. Instytut Mechaniki Stosowanej

Tematy:

medical robot
natural vibration
buckling
optimization
genetic algorithm
Pareto front
robot medyczny
drgania własne
wyboczenie
optymalizacja
algorytm genetyczny
front Pareto

Opis:

Medical robots with an instant center of rotation mechanism in a trocar are used for operating a human body or servicing artificial organs. The result of the work is the development of a multi-criteria optimization model of a discussed medical robot, considering safety factor, first eigenfrequency and buckling coefficient as a criteria. The article also analyzes two issues of mechanics, the natural frequency and linear buckling. A discrete mesh model of a novel robot design with ten degrees of freedom and ended with a scalpel was developed based on finite element method. For the given loads and supports, a multi-criteria optimization model was evolved, which was solved by using the response surface method and the multi-objective genetic algorithm. The results section shows the Pareto fronts for the criteria and geometrical dimensions of the kinematic chain. The courses of resonant vibrations and buckling strains were also characterized. The solved optimization model gives correct values for the adopted criteria. The values of resonance were defined, which makes it possible to select mechatronic drive systems in terms of the input they generate. Variability of the resonant vibrations phenomena, as well as shapes and directions of buckling, provide information about the displacements taking place in the medical robot system.

Źródło:

Vibrations in Physical Systems; 2020, 31, 3; art. no. 2020306
0860-6897

Pojawia się w:

Vibrations in Physical Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 95.

Tytuł:

Travel management optimization based on air pollution condition using Markov decision process and genetic algorithm (case study: Shiraz city)

Autorzy:

Bagheri, Mohammad
Ghafourian, Hossein
Kashefiolasl, Morteza
Pour, Mohammad Taghi Sadati
Rabbani, Mohammad

Powiązania:

https://bibliotekanauki.pl/articles/223520.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

air pollution
dynamic optimization
genetic algorithm
Markov decision-making process
zarządzanie transportem
optymalizacja
zanieczyszczenie powietrza
algorytm genetyczny
proces decyzyjny Markowa

Opis:

Currently, air pollution and energy consumption are the main issues in the transportation area in large urban cities. In these cities, most people choose their transportation mode according to corresponding utility including traveller's and trip’s characteristics. Also, there is no effective solution in terms of population growth, urban space, and transportation demands, so it is essential to optimize systematically travel demands in the real network of roads in urban areas, especially in congested areas. Travel Demand Management (TDM) is one of the well-known ways to solve these problems. TDM defined as a strategy that aims to maximize the efficiency of the urban transport system by granting certain privileges for public transportation modes, Enforcement on the private car traffic prohibition in specific places or times, increase in the cost of using certain facilities like parking in congested areas. Network pricing is one of the most effective methods of managing transportation demands for reducing traffic and controlling air pollution especially in the crowded parts of downtown. A little paper may exist that optimize urban transportations in busy parts of cities with combined Markov decision making processes with reward and evolutionary-based algorithms and simultaneously considering customers’ and trip’s characteristics. Therefore, we present a new network traffic management for urban cities that optimizes a multi-objective function that related to the expected value of the Markov decision system’s reward using the Genetic Algorithm. The planned Shiraz city is taken as a benchmark for evaluating the performance of the proposed approach. At first, an analysis is also performed on the impact of the toll levels on the variation of the user and operator cost components, respectively. After choosing suitable values for the network parameters, simulation of the Markov decision process and GA is dynamically performed, then the optimal decision for the Markov decision process in terms of total reward is obtained. The results illustrate that the proposed cordon pricing has significant improvement in performance for all seasons including spring, autumn, and winter.

Źródło:

Archives of Transport; 2020, 53, 1; 89-102
0866-9546
2300-8830

Pojawia się w:

Archives of Transport

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 96.

Tytuł:

Global path planning for multiple AUVs using GWO

Autorzy:

Panda, Madhusmita
Das, Bikramaditya
Pati, Bibhuti

Powiązania:

https://bibliotekanauki.pl/articles/229749.pdf

Data publikacji:

2020

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

Autonomous Underwater Vehicle
AUV
Genetic Algorithm
GA
Global Path Planning
GPP
Grey Wolf Optimization
GWO
Sliding Mode Control
SMC
waypoints

Opis:

In global path planning (GPP), an autonomous underwater vehicle (AUV) tracks a predefined path. The main objective of GPP is to generate a collision free sub-optimal path with minimum path cost. The path is defined as a set of segments, passing through selected nodes known as waypoints. For smooth planar motion, the path cost is a function of the path length, the threat cost and the cost of diving. Path length is the total distance travelled from start to end point, threat cost is the penalty of collision with the obstacle and cost of diving is the energy expanse for diving deeper in ocean. This paper addresses the GPP problem for multiple AUVs in formation. Here, Grey Wolf Optimization (GWO) algorithm is used to find the suboptimal path for multiple AUVs in formation. The results obtained are compared to the results of applying Genetic Algorithm (GA) to the same problem. GA concept is simple to understand, easy to implement and supports multi-objective optimization. It is robust to local minima and have wide applications in various fields of science, engineering and commerce. Hence, GA is used for this comparative study. The performance analysis is based on computational time, length of the path generated and the total path cost. The resultant path obtained using GWO is found to be better than GA in terms of path cost and processing time. Thus, GWO is used as the GPP algorithm for three AUVs in formation. The formation follows leader-follower topography. A sliding mode controller (SMC) is developed to minimize the tracking error based on local information while maintaining formation, as mild communication exists. The stability of the sliding surface is verified by Lyapunov stability analysis. With proper path planning, the path cost can be minimized as AUVs can reach their target in less time with less energy expanses. Thus, lower path cost leads to less expensive underwater missions.

Źródło:

Archives of Control Sciences; 2020, 30, 1; 77-100
1230-2384

Pojawia się w:

Archives of Control Sciences

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 97.

Tytuł:

Multicriteria optimization method of LNG distribution

Autorzy:

Chłopińska, E.
Gucma, M.

Powiązania:

https://bibliotekanauki.pl/articles/117110.pdf

Data publikacji:

2020

Wydawca:

Uniwersytet Morski w Gdyni. Wydział Nawigacyjny

Tematy:

cargo handling
Liquefied Natural Gas (LNG)
Multicriteria Optimization Method
LNG Distribution
Marine Diesel Oil (MDO)
Heavy Fuel Oil (HFO)
Vector Evaluated Genetic Algorithms (VEGA)
Multi-Objective Genetic Algorithm (MOGA)

Opis:

Liquefied Natural Gas (LNG) is considered as a realistic substation of marine fuel in 21 century. Solution of building new engines or converting diesels into gas fueled propulsion meets the stringent international emission regulations. For HFO (heavy fuel oil) or MDO (marine diesel oil) propelled vessels, operation of bunkering is relatively wide known and simple. Its due to the fact that fuel itself doesn’t require high standards of handling. Where for LNG as a fuel its very demanding process – it evaporates and requires either consuming by bunker vessel or reliquefication. Distribution of such bunker is becoming multidimensional problem with time and space constrains. The objective of the article is to review the methods of optimization using genetic algorithms for a model of LNG distribution. In particular, there will be considered methods of solving problems with many boundry criteria whose objective functions are contradictory. Methods used for solving the majority of problems are can prevent the simultaneous optimization of the examined objectives, e.g. the minimisation of costs or distance covered, or the maximisation of profits or efficiency etc. Here the standard genetic algorithms are suitable for solving multi-criteria problems by using functions producing a diversity of results depending on the adopted approach.

Źródło:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2020, 14, 2; 493-497
2083-6473
2083-6481

Pojawia się w:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 98.

Tytuł:

Application of genetic algorithm for double-lap adhesive joint design

Autorzy:

Kurennov, Sergei
Barakhov, Konstantin
Polyakov, Olexander
Taranenko, Igor

Powiązania:

https://bibliotekanauki.pl/articles/27309876.pdf

Data publikacji:

2023

Wydawca:

Polska Akademia Nauk. Czasopisma i Monografie PAN

Tematy:

adhesive joint
genetic algorithm
optimization
finite difference method
Goland-Reissner model
złącze klejowe
algorytm genetyczny
optymalizacja
metoda różnic skończonych
Model Golanda-Reissnera

Opis:

The problem of optimal design of symmetrical double-lap adhesive joint is considered. It is assumed that the main plate has constant thickness, while the thickness of the doublers can vary along the joint length. The optimization problem consists in finding optimal length of the joint and an optimal cross-section of the doublers, which provide minimum structural mass at given strength constraints. The classical Goland-Reissner model was used to describe the joint stress state. A corresponding system of differential equations with variable coefficients was solved using the finite difference method. Genetic optimization algorithm was used for numerical solution of the optimization problem. In this case, Fourier series were used to describe doubler thickness variation along the joint length. This solution ensures smoothness of the desired function. Two model problems were solved. It is shown that the length and optimal shape of the doubler depend on the design load.

Źródło:

Archive of Mechanical Engineering; 2023, LXX, 1; 27--42
0004-0738

Pojawia się w:

Archive of Mechanical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 99.

Tytuł:

Multi-objective optimization of a medical robot model in transient states

Autorzy:

Ilewicz, G.
Harlecki, A.

Powiązania:

https://bibliotekanauki.pl/articles/196543.pdf

Data publikacji:

2018

Wydawca:

Politechnika Śląska. Wydawnictwo Politechniki Śląskiej

Tematy:

medical robot
dynamics
transient state
optimization
genetic algorithm
finite element method
robot medyczny
dynamika
stan przejściowy
optymalizacja
algorytm genetyczny
metoda elementów skończonych

Opis:

The article describes the method for the multi-objective optimization of a proposed medical robot model, which has been considered in the form of a serial kinematic chain. In the assumed approach, the finite element method was used in order to model the flexibility of manipulator links. To speed up the optimization process, the response surface method was applied, defining the so-called metamodel. In order to uncover the optimal solution, a multi-objective genetic algorithm was used, guaranteeing the optimality of the manipulator model in the Pareto sense. The optimization process was carried out by analysing the selected case of the manipulator’s dynamics. The proposed optimization method allows us to minimize the mass of the manipulator while additionally ensuring the highest possible stiffness of its structure and sufficient strength of its parts. Furthermore, it offers the possibility to eliminate the natural frequency of vibrations of the model close to the resonant frequency.

Źródło:

Zeszyty Naukowe. Transport / Politechnika Śląska; 2018, 99; 79-88
0209-3324
2450-1549

Pojawia się w:

Zeszyty Naukowe. Transport / Politechnika Śląska

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 100.

Tytuł:

Cross‐Comparison of Evolutionary Algorithms for Optimizing Design of Sustainable Supply Chain Network under Disruption Risks

Autorzy:

Al-Zuheri, Atiya

Powiązania:

https://bibliotekanauki.pl/articles/2023790.pdf

Data publikacji:

2021

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

comparison
genetic algorithm
particle swarm optimization
sustainable supply chain design
disruption risk
porównanie
algorytm genetyczny
optymalizacja rojem cząstek
projektowanie zrównoważonego łańcucha dostaw
ryzyko zakłóceń

Opis:

Optimization of a sustainable supply chain network design (SSCND) is a complex decision-making process which can be done by the optimal determination of a set of decisions and constraints such as the selection of suppliers, transportation-related facilities and distribution centres. Different optimization techniques have been applied to handle various SSCND problems. Meta- heuristic algorithms are developed from these techniques that are commonly used to solving supply chain related problems. Among them, Genetic algorithms (GA) and particle swarm optimization (PSO) are implemented as optimization solvers to obtain supply network design decisions. This paper aims to compare the performance of these two evolutionary algorithms in optimizing such problems by minimizing the total cost that the system faces to potential disruption risks. The mechanism and implementation of these two evolutionary algorithms is presented in this paper. Also, using an optimization considers ordering, purchasing, inventory, transportation, and carbon tax cost, a numerical real-life case study is presented to demonstrate the validity of the effectiveness of these algorithms. A comparative study for the algorithms performance has been carried out based on the quality of the obtained solution and the results indicate that the GA performs better than PSO in finding lower-cost solution to the addressed SSCND problem. Despite a lot of research literature being done regarding these two algorithms in solving problems of SCND, few studies have compared the optimization performance between GA and PSO, especially the design of sustainable systems under risk disruptions.

Źródło:

Advances in Science and Technology. Research Journal; 2021, 15, 4; 342-351
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

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