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 simplistic regression-based genetic algorithm optimization of tool-work interface temperature

Autorzy:

Utkarsh
Kumar, Saumya
Kumar, Saaransh
Kiran, G. Uday
Mukhopadhyay, Arkadeb
Barman, Manik

Powiązania:

https://bibliotekanauki.pl/articles/38884187.pdf

Data publikacji:

2022

Wydawca:

Instytut Podstawowych Problemów Techniki PAN

Tematy:

turning
tool-work thermocouple
cutting temperature
ANOVA

Opis:

This work aims to investigate the average tool-work interface temperature for the HSS tool and AISI 1040 steel pair. A tool-work thermocouple is proposed for the measurement of temperature because of its simple construction in addition to the low cost. The machining process of AISI 1040 steel is considered due to its extensive application, including industry usage. The changes in cutting temperature are studied for combinations of cutting speed, feed and the depth of cut during turning operation. The orthogonal array L₉ by Taguchi is adopted for designing the experiments within a restricted set of runs. The average cutting temperature shows an increasing curve with functions of speed versus depth of cut and speed versus feed. But no clear trend is observed for a combination of feed versus depth of cut. A second-order regression equation with reasonable accuracy (R² = 0.99) is fitted using the data. Analysis of variance (ANOVA) reveals the highest contribution from cutting speed, which influences average temperature at the interface of tool and work. Further, the genetic algorithm predicts an optimal combination of parameters, which is 82.542 m/min cutting speed, 0.276 mm/rev feed rate and 0.2 mm depth.

Źródło:

Engineering Transactions; 2022, 70, 2; 141-156
0867-888X

Pojawia się w:

Engineering Transactions

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 3.

Tytuł:

Automatic identification of malfunctions of large turbomachinery during transient states with genetic algorithm optimization

Autorzy:

Barszcz, Tomasz
Zabaryłło, Mateusz

Powiązania:

https://bibliotekanauki.pl/articles/2052104.pdf

Data publikacji:

2022

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

machine learning
fault detection
transient
turbine generator
genetic algorithm

Opis:

Turbines and generators operating in the power generation industry are a major source of electrical energy worldwide. These are critical machines and their malfunctions should be detected in advance in order to avoid catastrophic failures and unplanned shutdowns. A maintenance strategy which enables to detect malfunctions at early stages of their existence plays a crucial role in facilities using such types of machinery. The best source of data applied for assessment of the technical condition are the transient data measured during start-ups and coast-downs. Most of the proposed methods using signal decomposition are applied to small machines with a rolling element bearing in steady-state operation with a shaft considered as a rigid body. The machines examined in the authors’ research operate above their first critical rotational speed interval and thus their shafts are considered to be flexible and are equipped with a hydrodynamic sliding bearing. Such an arrangement introduces significant complexity to the analysis of the machine behavior, and consequently, analyzing such data requires a highly skilled human expert. The main novelty proposed in the paper is the decomposition of transient vibration data into components responsible for particular failure modes. The method is automated and can be used for identification of turbogenerator malfunctions. Each parameter of a particular decomposed function has its physical representation and can help the maintenance staff to operate the machine properly. The parameters can also be used by the managing personnel to plan overhauls more precisely. The method has been validated on real-life data originating from a 200 MW class turbine. The real-life field data, along with the data generated by means of the commercial software utilized in GE’s engineering department for this particular class of machines, was used as the reference data set for an unbalanced response during the transients in question.

Źródło:

Metrology and Measurement Systems; 2022, 29, 1; 175-190
0860-8229

Pojawia się w:

Metrology and Measurement Systems

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 4.

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: 5.

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: 6.

Tytuł:

Application of genetic algorithm for optimization the safety system of the nuclear reactor

Autorzy:

El-Sayed Wahed, M.
Ibrahim, W. Z.
Effat, A. M.

Powiązania:

https://bibliotekanauki.pl/articles/146492.pdf

Data publikacji:

2009

Wydawca:

Instytut Chemii i Techniki Jądrowej

Tematy:

genetic algorithm
non-dominated sorting
chimney water injection system (CWIS)
Egypt nuclear

Opis:

The purpose of this paper is to present an approach to optimization in which every target is considered as a separate objective to be optimized. Multi-objective optimization is a powerful tool for resolving conflicting objectives in engineering design and numerous other fields. One approach to solve multi-objective optimization problems is the non-dominated sorting genetic algorithm (NSGA). Genetic algorithm (GA) was applied in regarding the choice of the time intervals for the periodic testing of the components of the chimney water injection system (CWIS) of the 22 MW open pool multipurpose reactor (MPR), ETRR-2, at the Egyptian Atomic Energy Authority, has been used as a case study.

Źródło:

Nukleonika; 2009, 54, 1; 51-56
0029-5922
1508-5791

Pojawia się w:

Nukleonika

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 7.

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: 8.

Tytuł:

Klasyczny algorytm genetyczny w dynamicznej optymalizacji modelu liniowego produkcji roślinnej
Classical genetic algorithm for dynamic optimization of the linear model of plants production

Autorzy:

Landowska, A.

Powiązania:

https://bibliotekanauki.pl/articles/78542.pdf

Data publikacji:

2011

Wydawca:

Zachodniopomorski Uniwersytet Technologiczny w Szczecinie. Wydawnictwo Uczelniane ZUT w Szczecinie

Opis:

Article presents application of classical genetic algorithm for the problem of dynamic optimization of the linear model. The model describes plants production during two years and takes into consideration plants changing. The soil in a good culture it is very important issue to obtain the highest crop. In the work the conditions for classical genetic algorithm to solving introduced problem are presented.

Źródło:

Folia Pomeranae Universitatis Technologiae Stetinensis. Oeconomica; 2011, 62
2081-0644

Pojawia się w:

Folia Pomeranae Universitatis Technologiae Stetinensis. Oeconomica

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 9.

Tytuł:

Efektywność modyfikacji algorytmu genetycznego w optymalizacji złożonych systemów oświetlenia elektrycznego
Efficiency of genetic algorithm modification of optimization of the electric light complex systems

Autorzy:

Tomczewski, A.

Powiązania:

https://bibliotekanauki.pl/articles/376404.pdf

Data publikacji:

2013

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

algorytm genetyczny
oświetlenie elektryczne
oświetlenie wnętrz
optymalizacja kosztów systemu oświetlenia

Opis:

W artykule podjęto tematykę modyfikacji metaheurystyki algorytmu genetycznego wykorzystanego do optymalizacji złożonego systemu oświetlenia wnętrz. Podano charakterystykę zadania, zastosowane kryterium oceny jakości rozwiązań oraz złożoność zagadnienia rzutującą na przebieg procesu optymalizacji. Przedstawiono modyfikacje związane bezpośrednio ze strukturą realizowanego zadania. Wykonano obliczenia optymalizacyjne dla obiektu testowego wykazujące wzrost efektywności metody w stosunku do algorytmu odniesienia.
The article shows the metaheurystics modification of a genetic algorithm used to optimize the lighting complex system. The characteristics of the tasks and the evaluation criterion of the quality of solutions and the complexity of the issues to bear on the process of optimization was presented. Modifications connected to the structure of the task executed was shown. Optimization calculations were performed for the test object, showing an increase of the efficiency of the method compared to a reference algorithm.

Źródło:

Poznan University of Technology Academic Journals. Electrical Engineering; 2013, 73; 173-182
1897-0737

Pojawia się w:

Poznan University of Technology Academic Journals. Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 10.

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: 11.

Tytuł:

Analiza porównawcza klasycznych metod optymalizacji i algorytmu genetycznego na przykładzie projektowania filtrów
Comparison Analysis of Classical Static Optimization Methods and Genetic Algorithm for Example of The Filter Design

Autorzy:

Rutczyńska-Wdowiak, K.
Stefański, T.

Powiązania:

https://bibliotekanauki.pl/articles/155008.pdf

Data publikacji:

2010

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

metody optymalizacji statycznej
algorytmy genetyczne
projektowanie filtrów
classical static optimization methods
genetic algorithms
design of filters

Opis:

W pracy przedstawiono analizę porównawczą metod klasycznych optymalizacji (Box'a i Nelder'a-Mead'a) oraz algorytmu genetycznego w problemie projektowania filtru cyfrowego na przykładzie jego prototypu analogowego. Badania koncentrowały się na określeniu wpływu wybranej metody, zadanych warunków startowych (przestrzeni poszukiwań) oraz kryterium minimalizacji i zatrzymania algorytmów na dokładność uzyskania optymalnego rozwiązania.
The purpose of the paper is to provide a basis for comparison between classical static optimization methods (Box and Nelder-Mead) and genetic algorithm regarding digital filters based on analog prototype. The analysis of optimization methods (genetic and classical) with regard to convergence and accuracy for the process of searching solution and time of numerical calculations was carried out. It is genetic algorithm, rather than classical static optimization method, that ensures greater probability of finding the global minimum of function. Application of numerical static optimization method is frequently limited due to instability of filter mathematical model during the process of analysis. From among other methods subjected to analysis it is only Box's method that enables the introduction of restrictions which ensure stability of the filter model. Furthermore, the local minimum of function instead of the global one is determined particularly in case of large number of parameters. The genetic algorithms through the random choice of a sufficient number of representative searches within the whole population of potential solutions and therefore the chance of determining the local minimum instead of a global one is considerably smaller than in case of using of classical method. On the other hand, the genetic algorithm requires more numerical calculations by comparison with Nelder-Mead's or Box's methods.

Źródło:

Pomiary Automatyka Kontrola; 2010, R. 56, nr 6, 6; 624-627
0032-4140

Pojawia się w:

Pomiary Automatyka Kontrola

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 12.

Tytuł:

Zastosowanie metod sztucznej inteligencji w energetyce
The applications of artificial intelligence techniques in energy systems

Autorzy:

Ściążko, A.

Powiązania:

https://bibliotekanauki.pl/articles/276892.pdf

Data publikacji:

2011

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

energetyka
sztuczna inteligencja
sieci neuronowe
algorytmy genetyczne
modelowanie
optymalizacja
odnawialne źródła energii
energy science
artificial intelligence
neural network
genetic algorithm
modeling
optimization
renewable energy

Opis:

Artykuł prezentuje możliwości wykorzystania metod sztucznej inteligencji w energetyce. Zastosowania te można podzielić na trzy grupy: modelowanie, przewidywanie i planowanie (optymalizacja) oraz kontrola procesów. W artykule pokazano typowe przykłady użycia sztucznej inteligencji, charakterystyczne dla każdej z grup. Przedstawiono także możliwe przyszłe wykorzystania tego typu metodologii, w szczególności w inteligentnych systemach elektroenergetycznych - Smart Grid. Druga część artykułu przedstawia i omawia przykład wykorzystania sztucznej inteligencji w modelowaniu systemu elektroenergetycznego, złożonego z następujących elementów: elektrociepłownia geotermalna, źródło geotermalne, miejska sieć ciepłownicza oraz zakład przemysłowy. Zadanie polegało na przygotowaniu modelu komputerowego rozważanego systemu oraz wielokryte-rialnej optymalizacji problemu. Jakość każdego z rozwiązań była oceniana na podstawie dwóch różnych funkcji dopasowania: obliczonej wartości kosztów inwestycyjnych oraz sprawności egzergetycznej systemu. Pokazano w jaki sposób można wykorzystać genetyczny algorytm optymalizacji wielokryterialnej oraz modelowanie zastępcze z wykorzystaniem sztucznej sieci neuronowej do analizy zadanego problemu. Rezultaty optymalizacji zostały zobrazowane na diagramie Pareto, na ich podstawie pokazano kilka typów możliwych rozwiązań projektowych (niewielkie koszty inwestycyjne i niska sprawność, wysokie koszty inwestycyjne ale wysoka sprawność oraz rozwiązanie pośrednie). Dla modelu zastępczego określono jego dokładność oraz dopasowanie do oczekiwanych rezultatów.
Paper presents possible applications of artificial intelligence techniques in the energy science problems. Those applications can be divided into three groups: modeling, predictions and planning (optimization) and process control. Article shows typical examples of use artificial intelligence in each group. Moreover there are presented the future possibilities of application, for example in Smart Grid's problems. Second part of the article introduces the case study of using artificial intelligence techniques in real life problem of analysis of complicated energy system. Its main elements are geothermal power plant, district heating system, industrial plant and electrical grid. The task was to model the system and run the multi criteria optimization. The quality of each solution was graded by the different objective functions: investment cost and energy efficiency. The paper presents multi criteria genetic optimization algorithm and neural network surrogate modeling for given problem. The optimization's results can be found in the Pareto diagram - they show different possible solutions (with low investment cost and low efficiency, with high cost and high efficiency or with mediumvalues). The quality of surrogate model is presented on the regression graph for neural network.

Źródło:

Pomiary Automatyka Robotyka; 2011, 15, 7-8; 53-59
1427-9126

Pojawia się w:

Pomiary Automatyka Robotyka

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 13.

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: 14.

Tytuł:

Particle Swarm Optimization Algorithm for Leakage Power Reduction in VLSI Circuits

Autorzy:

Leela Rani, V.
Madhavi Latha, M.

Powiązania:

https://bibliotekanauki.pl/articles/225990.pdf

Data publikacji:

2016

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

leakage power
PSO algorithm
genetic algorithm
minimum leakage vector
Verilog-HDL implementation

Opis:

Leakage power is the dominant source of power dissipation in nanometer technology. As per the International Technology Roadmap for Semiconductors (ITRS) static power dominates dynamic power with the advancement in technology. One of the well-known techniques used for leakage reduction is Input Vector Control (IVC). Due to stacking effect in IVC, it gives less leakage for the Minimum Leakage Vector (MLV) applied at inputs of test circuit. This paper introduces Particle Swarm Optimization (PSO) algorithm to the field of VLSI to find minimum leakage vector. Another optimization algorithm called Genetic algorithm (GA) is also implemented to search MLV and compared with PSO in terms of number of iterations. The proposed approach is validated by simulating few test circuits. Both GA and PSO algorithms are implemented in Verilog HDL and the simulations are carried out using Xilinx 9.2i. From the simulation results it is found that PSO based approach is best in finding MLV compared to Genetic based implementation as PSO technique uses less runtime compared to GA. To the best of the author’s knowledge PSO algorithm is used in IVC technique to optimize power for the first time and it is quite successful in searching MLV.

Źródło:

International Journal of Electronics and Telecommunications; 2016, 62, 2; 179-186
2300-1933

Pojawia się w:

International Journal of Electronics and Telecommunications

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 15.

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: 16.

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: 17.

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: 18.

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: 19.

Tytuł:

Optimizing the Seakeeping Performance of Ship Hull Forms Using Genetic Algorithm

Autorzy:

Bagheri, L.
Ghassemi, H.
Dehghanian, A.

Powiązania:

https://bibliotekanauki.pl/articles/117211.pdf

Data publikacji:

2014

Wydawca:

Uniwersytet Morski w Gdyni. Wydział Nawigacyjny

Tematy:

genetic algorithms
Seakeeping Performance
Ship Hull
Hydrodynamics
Ship Design
Froude Number
Seakeeping Calculation
optimization

Opis:

Hull form optimization from a hydrodynamic performance point of view is an important aspect of ship design. This study presents a computational method to estimate the ship seakeeping in regular head wave. In the optimization process the Genetic Algorithm (GA) is linked to the computational method to obtain an optimum hull form by taking into account the displacement as design constraint. New hull forms are obtained from the well-known S60 hull and the classical Wigley hull taken as initial hulls in the optimization process at two Froude numbers (Fn=0.2 and Fn=0.3). The optimization variables are a combination of ship hull offsets and main dimensions. The objective function of the optimization procedure is the peak values for vertical absolute motion at a point 0.15LBP behind the forward perpendicular, in regular head waves.

Źródło:

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation; 2014, 8, 1; 49-57
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: 20.

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: 21.

Tytuł:

Fine optimization of rigid frame bridge parameters based on the genetic algorithm

Autorzy:

Lu, Yao
Li, Dejian
Yao, Che
Li, Zhen

Powiązania:

https://bibliotekanauki.pl/articles/2034015.pdf

Data publikacji:

2021

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Opis:

The primary aim of this paper is to study the optimization of rigid frame bridge parameters. With a three-span continuous rigid frame bridge as the engineering background, finite element models were established. Then an index about bridge force condition was proposed to calculate the optimal side-to-mid span ratio with different side-to-mid span ratio parameters. Based on the ratio, the values of the girder depth at the pier and the bottom curve degree of the box-girder were taken as parameters in their common ranges for further optimization. A comprehensive multi-objective evaluation index correlated with the mid-span section stress, the mid-span deflection, and the concrete consumption was proposed to do fine optimization through the genetic algorithm method. The result of this study shows that the genetic algorithm is an effective method for bridge optimization and could provide better girder design parameter combinations for the comprehensive performance, and the optimal result could be obtained in the continuous parameter definition domains. It also shows that a larger girder depth at the pier to span ratio and a smaller curve degree in their common ranges should be taken for the bridge’s comprehensive performance.

Źródło:

Archives of Civil Engineering; 2021, 67, 4; 261-272
1230-2945

Pojawia się w:

Archives of Civil Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 22.

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: 23.

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: 24.

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: 25.

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: 26.

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: 27.

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: 28.

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: 29.

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: 30.

Tytuł:

Genetic algorithm as a method of solving selected optimization problems

Autorzy:

Gil, J.

Powiązania:

https://bibliotekanauki.pl/articles/225536.pdf

Data publikacji:

2011

Wydawca:

Politechnika Warszawska. Wydział Geodezji i Kartografii

Tematy:

algorytmy genetyczne
genetic algorithms

Opis:

Genetic algorithms, which were created on the basis of observation and imitation of processes happening in living organisms, are used to solve optimisation tasks. The idea of genetic algorithms was presented by Holland, and they were developed and implemented for solving optimisation tasks by Goldberg. Choice of particular variables of the vector w = [w1, w2,…, w n ] in order to maximize or minimize a fitness function takes place as a result of a sequence of genetic operations in the form of selection, crossbreeding and mutation. The article describes the basic genetic (classic) algorithm including its components.

Źródło:

Reports on Geodesy; 2011, z. 1/90; 141-147
0867-3179

Pojawia się w:

Reports on Geodesy

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 31.

Tytuł:

Optimization the dynamical parameters of three phase induction motor using genetic algorithm

Autorzy:

Mohammed, M.H.

Powiązania:

https://bibliotekanauki.pl/articles/376222.pdf

Data publikacji:

2012

Wydawca:

Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej

Tematy:

induction motor
genetic algorithm
differential evolution
DE

Opis:

This paper deals with the optimization of the induction motor design with respect to torque as a dynamical parameter. Most studies on the design of an induction motor using optimization techniques are concerned with the minimization of the motor cost and describe the optimization technique that was employed, giving the results of a single (or several) optimal design(s).Procedure includes the relationship between torque of motor and other effects as they occur in an optimal design. The optimization method that was used in this paper is Differential Evolution as genetic algorithm. Optimal results are in picture as curves or in tabula.

Źródło:

Poznan University of Technology Academic Journals. Electrical Engineering; 2012, 72; 123-128
1897-0737

Pojawia się w:

Poznan University of Technology Academic Journals. Electrical Engineering

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 32.

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: 33.

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: 34.

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: 35.

Tytuł:

Sensor Network Deployment Optimization for Improved Area Coverage Using a Genetic Algorithm

Autorzy:

Szklarski, Łukasz

Powiązania:

https://bibliotekanauki.pl/articles/1861643.pdf

Data publikacji:

2016

Wydawca:

Wyższa Szkoła Bezpieczeństwa Publicznego i Indywidualnego Apeiron w Krakowie

Tematy:

network of sensors
genetic algorithm
sensors deployment
area coverage

Opis:

Ensuring optimal coverage is a central objective of every sensor deployment plan. Effective monitoring of the environment helps to minimize manpower and time, while enhancing surveillance capability. In this paper, a solution for improved area coverage was presented. A lattice of a pre-defined parameter has been used as an input for the algorithm. For the purpose of the research the blanket deployment strategy has been adopted. Then, a genetic algorithm has been proposed and implemented to find an optimal solution. The proposed approach has been tested and the conclusions have been drawn. The results proved that the proposed genetic algorithm could already provide satisfactory results, usually finding only suboptimal solutions.

Źródło:

Security Dimensions; 2016, 19(19); 150-181
2353-7000

Pojawia się w:

Security Dimensions

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 36.

Tytuł:

Optimization Design of Hybrid Mufflers on Broadband Frequencies Using the Genetic Algorithm

Autorzy:

Chiu, M-C.

Powiązania:

https://bibliotekanauki.pl/articles/177052.pdf

Data publikacji:

2011

Wydawca:

Polska Akademia Nauk. Czytelnia Czasopism PAN

Tematy:

dissipative
reactive
hybrid muffler
genetic algorithm
space constraints

Opis:

Recently, there has been research on high frequency dissipative mufflers. However, research on shape optimization of hybrid mufflers that reduce broadband noise within a constrained space is sparse. In this paper, a hybrid muffler composed of a dissipative muffler and a reactive muffler within a constrained space is assessed. Using the eigenvalues and eigenfunctions, a coupling wave equation for the perforated dissipative chamber is simplified into a four-pole matrix form. To efficiently find the optimal shape within a constrained space, a four-pole matrix system used to evaluate the acoustical performance of the sound transmission loss (STL) is eval- uated using a genetic algorithm (GA). A numerical case for eliminating a broadband venting noise is also introduced. To verify the reliability of a GA optimization, optimal noise abatements for two pure tones (500 Hz and 800 Hz) are exemplified. Before the GA operation can be carried out, the accuracy of the mathematical models has been checked using experimental data. Results indicate that the maximal STL is precisely located at the desired target tone. The optimal result of case studies for eliminating broadband noise also reveals that the overall sound power level (SWL) of the hybrid muffler can be reduced from 138.9 dB(A) to 84.5 dB(A), which is superior to other mufflers (a one-chamber dissipative and a one-chamber reactive muffler). Consequently, a successful approach used for the optimal design of the hybrid mufflers within a constrained space has been demonstrated.

Źródło:

Archives of Acoustics; 2011, 36, 4; 795-822
0137-5075

Pojawia się w:

Archives of Acoustics

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 37.

Tytuł:

Optimization of fractal compression of 3d images using a genetic algorithm

Autorzy:

Khanmirza, Z
Ramezani, F
Motameni, H

Powiązania:

https://bibliotekanauki.pl/articles/102068.pdf

Data publikacji:

2015

Wydawca:

Stowarzyszenie Inżynierów i Techników Mechaników Polskich

Tematy:

stereo system
fractal compression
genetic algorithm

Opis:

3D image technologies are widely recognized as the next generation of visual presentation considering the achievement of more natural experiences. To produce such images, two cameras are placed in a bit different position. When we seek to compress such images, we need a procedure to compress two images synchronously. In this paper, a procedure is presented for a suitable compression based on fractal compression which shows that we obtain high compression rate with an appropriate image quality; however, since the proposed procedure has a low search speed, we used genetic algorithm to remove the case.

Źródło:

Advances in Science and Technology. Research Journal; 2015, 9, 26; 124-128
2299-8624

Pojawia się w:

Advances in Science and Technology. Research Journal

Dostawca treści:

Biblioteka Nauki

/5

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Skocz do pozycji: 38.

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: 39.

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: 40.

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: 41.

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: 42.

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: 43.

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: 44.

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: 45.

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: 46.

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: 47.

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: 48.

Tytuł:

Optimization of a modular neural network for pattern recognition using parallel genetic algorithm

Autorzy:

Cárdenas, M.
Melin, P.
Cruz, L.

Powiązania:

https://bibliotekanauki.pl/articles/384887.pdf

Data publikacji:

2011

Wydawca:

Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów

Tematy:

modular neural networks
parallel genetic algorithm
multi-core

Opis:

In this paper, the implementation of a Parallel Genetic Algorithm (PGA) for the training stage, and the optimi zation of a monolithic and modular neural network, for pattern recognition are presented. The optimization con sists in obtaining the best architecture in layers, and neu rons per layer achieving the less training error in a shor ter time. The implementation was performed in a multicore architecture, using parallel programming techniques to exploit its resources. We present the results obtained in terms of performance by comparing results of the training stage for sequential and parallel implementations.

Źródło:

Journal of Automation Mobile Robotics and Intelligent Systems; 2011, 5, 1; 77-84
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: 49.

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: 50.

Tytuł:

Optimization of travel routes based on modified genetic and ant algorithms

Autorzy:

Rybchak, Z.

Powiązania:

https://bibliotekanauki.pl/articles/410861.pdf

Data publikacji:

2017

Wydawca:

Polska Akademia Nauk. Oddział w Lublinie PAN

Tematy:

ant algorithm
ant colony genetic algorithm
hiking trails
algorytm mrówkowy
genetyczny algorytm mrówkowy
ścieżki turystyczne

Opis:

In the article, propose to use modified mating operators and initialization genetic and ant algorithms to solve transport problems in tourism. The article analyzes modern methods of optimization of routes used to transport tourists between the settlements of view of efficient use of resources. By analyzing the behavior of ant colonies, such as finding the shortest route by providing mating pheromones and features two solutions genetic algorithm developed algorithms for finding the optimal route, costing resources search distance, time, route, storing executed routes. The paper present description created system for mobile phones operating system IOS, which performs all operations listed above.

Źródło:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 85-90
2084-5715

Pojawia się w:

ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes

Dostawca treści:

Biblioteka Nauki

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