Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "Multi-objective Optimization" wg kryterium: Temat

  Lista filtrów
Wyrównaj
Wyświetlanie 1-15 z 55
Tytuł:
Multi-objective conceptual design optimization of a domestic unmanned airship
Autorzy:
Amani, S.
Pourtakdoust, S. H.
Pazooki, F.
Powiązania:
https://bibliotekanauki.pl/articles/949225.pdf
Data publikacji:
2014
Wydawca:
Polskie Towarzystwo Mechaniki Teoretycznej i Stosowanej
Tematy:
airship
multi-objective optimization
Pareto optimality
Opis:
Autonomous airships have gained a high degree of importance over the last decades, both theoretically as well and practically. This is due to their long endurance capability needed for monitoring, observation and communication missions. In this paper, a Multi-Objective Optimization approach (MOO) is followed for conceptual design of an airship taking aerody- namic drag, static stability, performance as well as the production cost that is proportional to the helium mass and the hull surface area, into account. Optimal interaction of the afo- rementioned disciplinary objectives is desirable and focused through the MOO analysis. Standard airship configurations are categorized into three major components that include the main body (hull), stabilizers (elevators and rudders) and gondola. Naturally, component sizing and positioning play an important role in the overall static stability and performance characteristics of the airship. The most important consequence of MOO analysis is that the resulting design not only meets the mission requirement, but will also be volumetrically optimal while having a desirable static and performance characteristics. The results of this paper are partly validated in the design and construction of a domestic unmanned airship indicating a good potential for the proposed approach.
Źródło:
Journal of Theoretical and Applied Mechanics; 2014, 52, 1; 47-60
1429-2955
Pojawia się w:
Journal of Theoretical and Applied Mechanics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Handling insensitivity in multi-physics inverse problems using a complex evolutionary strategy
Rozpoznawanie niewrażliwości w wielokryterialnych problemach odwrotnych przy użyciu złożonej strategii ewolucyjnej
Autorzy:
Sawicki, Jakub
Smołka, Maciej
Łoś, Marcin
Schaefer, Robert
Powiązania:
https://bibliotekanauki.pl/articles/29520322.pdf
Data publikacji:
2019
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
multi-objective optimization
evolutionary algorithm
inverse problem
Opis:
In this paper we present a complex strategy for the solution of ill posed, in-verse problems formulated as multiobjective global optimization ones. The strategy is capable of identifying the shape of objective insensitivity regions around connected components of Pareto set. The goal is reached in two phases. In the first, global one, the connected components of the Pareto set are localized and separated in course of the multi-deme, hierarchic memetic strategy HMS. In the second, local phase, the random sample uniformly spread over each Pareto component and its close neighborhood is obtained in the specially profiled evolutionary process using multiwinner selection. Finally, each local sample forms a base for the local approximation of a dominance function. Insensitivity region surrounding each connected component of the Pareto set is estimated by a sufficiently low level set of this approximation. Capabilities of the whole procedure was verified using specially-designed two-criterion benchmarks.
Artykuł prezentuje złożoną strategię rozwiązywania źle postawionych problemów odwrotnych sformułowanych jako wielokryterialne zadania optymalizacji globalnej. Opisana strategia umożliwia identyfikację obszarów niewrażliwości funkcji celu wokół spójnych składowych zbioru Pareto. Cel jest osiągany w dwu etapach. W pierwszym z nich — globalnym — składowe spójne zbioru Pareto są lokalizowane i separowane przy pomocy wielopopulacyjnej hierarchicznej strategii memetycznej HMS. W etapie drugim — lokalnym — przy użyciu specjalnie sprofilowanego procesu ewolucyjnego wykorzystującego operator selekcji wyborczej z wieloma zwycięzcami produkowana jest losowa próbka rozłożona jednostajnie na każdej składowej i jej bliskim otoczeniu. Finalnie każda lokalna próbka jest użyta jako baza do zbudowania lokalnej aproksymacji funkcji dominacji. Zbiory poziomicowe tej aproksymacji dla odpowiednio niskich poziomów stanowią przybliżenie zbiorów niewrażliwości wokół składowych spójnych. Możliwości strategii zostały zweryfikowane przy użyciu specjalnie zaprojektowanych dwukryterialnych funkcji testowych.
Źródło:
Computer Methods in Materials Science; 2019, 19, 1; 2-11
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
The scalarization approach for multi-objective optimization of network resource allocation in distributed systems
Autorzy:
Wesołowski, Z.
Powiązania:
https://bibliotekanauki.pl/articles/92817.pdf
Data publikacji:
2016
Wydawca:
Uniwersytet Przyrodniczo-Humanistyczny w Siedlcach
Tematy:
distributed system
resource allocation
multi-objective optimization
Opis:
The paper presents a multi-objective optimization framework to the network resource allocation problem, where the aim is to maximize the bitrates of data generated by all agents executed in a distributed system environment. In the proposed approach, the utility functions of agents may have different forms, which allows a more realistic modeling of phenomena occurring in computer networks. A scalarizing approach has been applied to solve the optimization problem.
Źródło:
Studia Informatica : systems and information technology; 2016, 1-2(20); 39-52
1731-2264
Pojawia się w:
Studia Informatica : systems and information technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Tuning of a Fuzzy System for Controlling Searching Process In Multi Objective Scheduling Immune Algorithm
Autorzy:
Wosik, I.
Skołud, B.
Powiązania:
https://bibliotekanauki.pl/articles/971215.pdf
Data publikacji:
2009
Wydawca:
Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Tematy:
Immune Algorithm
multi objective optimization
fuzzy system
Opis:
In the paper the Multi Objective Immune Algorithm (MOIA) for an open job shop scheduling problem (OJSP) is proposed. The OJSP belongs to most both time consuming and most complicated problems in scope of searching space. In the paper schedules are evaluated by using three criteria: makespan, flowtime and total tardiness. MOIA proposes a schedule, which is best one, selected from a set of achieved solutions. An affinity threshold is a parameter that controls equilibrium between searching space and solutions diversity in MOIA. The affinity threshold is defined by using fuzzy logic system. In the paper fuzzy system is tuned by selecting shape, size of fuzzy sets, and fuzzy decisions of an affinity threshold. If the fuzzy system is used then neither the knowledge about the affinity threshold nor influence over searching processes is not required from a decision-maker. The application of the fuzzy system makes the process of decision-making user friendly. In the paper efficiency of MOIA before and after the fuzzy system tuning is compared and computational results are presented.
Źródło:
Journal of Machine Engineering; 2009, 9, 1; 130-143
1895-7595
2391-8071
Pojawia się w:
Journal of Machine Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Single spiking neuron multi-objective optimization for pattern classification
Autorzy:
Juarez-Santini, Carlos
Ornelas-Rodriguez, Manuel
Soria-Alcaraz, Jorge Alberto
Rojas-Domínguez, Alfonso
Puga-Soberanes, Hector J.
Espinal, Andrés
Rostro-Gonzalez, Horacio
Powiązania:
https://bibliotekanauki.pl/articles/385022.pdf
Data publikacji:
2020
Wydawca:
Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:
multi-objective optimization
spiking neuron
pattern classification
Opis:
As neuron models become more plausible, fewer computing units may be required to solve some problems; such as static pattern classification. Herein, this problem is solved by using a single spiking neuron with rate coding scheme. The spiking neuron is trained by a variant of Multi-objective Particle Swarm Optimization algorithm known as OMOPSO. There were carried out two kind of experiments: the first one deals with neuron trained by maximizing the inter distance of mean firing rates among classes and minimizing standard deviation of the intra firing rate of each class; the second one deals with dimension reduction of input vector besides of neuron training. The results of two kind of experiments are statistically analyzed and compared again a Mono-objective optimization version which uses a fitness function as a weighted sum of objectives.
Źródło:
Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 1; 73-80
1897-8649
2080-2145
Pojawia się w:
Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
New solutions to a multi-objective benchmark problem of induction heating: an application of computational biogeography and evolutionary algorithms
Autorzy:
Di Barba, P.
Dughiero, F.
Forzan, M.
Mognaschi, M. E.
Sieni, E.
Powiązania:
https://bibliotekanauki.pl/articles/140925.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
induction heating
multi-physics analysis
multi-objective optimization
benchmark
Opis:
In induction heating the design of the inductor implies the solution of coupled electromagnetic and thermal fields, along with the use of optimal design procedures to identify the best possible device or process. The benchmark model proposed, a graphite disk heated by means of induction, is optimized using different optimization algorithms. The design aim requires to achieve a prescribed and uniform temperature distribution in the workpiece maximizing the system efficiency.
Źródło:
Archives of Electrical Engineering; 2018, 67, 1; 139-149
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A Survey on multi-objective based parameter optimization for deep learning
Autorzy:
Chakraborty, Mrittika
Pal, Wreetbhas
Bandyopadhyay, Sanghamitra
Maulik, Ujjwal
Powiązania:
https://bibliotekanauki.pl/articles/27312917.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
deep learning
multi-objective optimization
parameter optimization
neural networks
Opis:
Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of the deep networks, therefore, requires improved optimization algorithms with high convergence rates. The single objective-based optimization methods generally used are mostly time-consuming and do not guarantee optimum performance in all cases. Mathematical optimization problems containing multiple objective functions that must be optimized simultaneously fall under the category of multi-objective optimization sometimes referred to as Pareto optimization. Multi-objective optimization problems form one of the alternatives yet useful options for parameter optimization. However, this domain is a bit less explored. In this survey, we focus on exploring the effectiveness of multi-objective optimization strategies for parameter optimization in conjunction with deep neural networks. The case studies used in this study focus on how the two methods are combined to provide valuable insights into the generation of predictions and analysis in multiple applications.
Źródło:
Computer Science; 2023, 24 (3); 327--359
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Information management in passenger traffic supporting system design as a multi-criteria discrete optimization task
Autorzy:
Galuszka, A.
Krystek, J.
Swierniak, A.
Lungoci, C.
Grzejszczak, T.
Powiązania:
https://bibliotekanauki.pl/articles/229171.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
multi objective optimization
discrete static optimization
Pareto solutions
integrated systems
Opis:
This paper presents a concept of an Integrated System of Supporting Information Management in Passenger Traffic (ISSIMPT). The novelty of the system is an integration of six modules: video monitoring, counting passenger flows, dynamic information for passengers, the central processing unit, surveillance center and vehicle diagnostics into one coherent solution. Basing on expert evaluations, we propose to present configuration design problem of the system as a multi-objectives discrete static optimization problem. Then, hybrid method joining properties of weighted sum and ε-constraint methods is applied to solve the problem. Solution selections based on hybrid method, using set of exemplary cases, are shown.
Źródło:
Archives of Control Sciences; 2017, 27, 2; 229-238
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Load Balancing Based on Optimization Algorithms: An Overview
Autorzy:
Mbarek, Fatma
Mosorov, Volodymyr
Powiązania:
https://bibliotekanauki.pl/articles/308122.pdf
Data publikacji:
2019
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
ant colony optimization
honey bee
load balancing
multi-objective optimization
Opis:
Combinatorial optimization challenges are rooted in real-life problems, continuous optimization problems, discrete optimization problems and other significant problems in telecommunications which include, for example, routing, design of communication networks and load balancing. Load balancing applies to distributed systems and is used for managing web clusters. It allows to forward the load between web servers, using several scheduling algorithms. The main motivation for the study is the fact that combinatorial optimization problems can be solved by applying optimization algorithms. These algorithms include ant colony optimization (ACO), honey bee (HB) and multi-objective optimization (MOO). ACO and HB algorithms are inspired by the foraging behavior of ants and bees which use the process to locate and gather food. However, these two algorithms have been suggested to handle optimization problems with a single-objective. In this context, ACO and HB have to be adjusted to multiobjective optimization problems. This paper provides a summary of the surveyed optimization algorithms and discusses the adaptations of these three algorithms. This is pursued by a detailed analysis and a comparison of three major scheduling techniques mentioned above, as well as three other, new algorithms (resulting from the combination of the aforementioned techniques) used to efficiently handle load balancing issues.
Źródło:
Journal of Telecommunications and Information Technology; 2019, 4; 3-12
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fuzzy multi-objective supplier selection problem in a supply chain
Autorzy:
Kamal, Murshid
Gupta, Srikant
Raina, Ather Aziz
Powiązania:
https://bibliotekanauki.pl/articles/1177774.pdf
Data publikacji:
2018
Wydawca:
Przedsiębiorstwo Wydawnictw Naukowych Darwin / Scientific Publishing House DARWIN
Tematy:
Fuzzy Goal Programming
Multi-objective Optimization
Supplier Selection Problem
Opis:
The decision making of supplier selection and their allocation is one of the main concerns in supply chain management. In this paper, an attempt has been made to obtain an optimal allocation for supplier based on minimizing the net cost, minimizing the net rejections, and minimizing the net late deliveries subject to realistic constraints regarding buyer’s demand, vendors’ capacity, vendors’ quota flexibility, purchase value of items, budget allocation to individual vendor, etc. We convert the problem into single objective fuzzy goal programming problem by using weighted root power mean the method of aggregation with linear, exponential and hyperbolic membership functions. The comparison has been made by assigning different weights to the objective functions. A numerical illustration is provided for the verification of applicability of the approach.
Źródło:
World Scientific News; 2018, 100; 165-183
2392-2192
Pojawia się w:
World Scientific News
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-objective heuristic feature selection for speech-based multilingual emotion recognition
Autorzy:
Brester, C.
Semenkin, E.
Sidorov, M.
Powiązania:
https://bibliotekanauki.pl/articles/91588.pdf
Data publikacji:
2016
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
multi-objective optimization
feature selection
speech-based emotion recognition
Opis:
If conventional feature selection methods do not show sufficient effectiveness, alternative algorithmic schemes might be used. In this paper we propose an evolutionary feature selection technique based on the two-criterion optimization model. To diminish the drawbacks of genetic algorithms, which are applied as optimizers, we design a parallel multicriteria heuristic procedure based on an island model. The performance of the proposed approach was investigated on the Speech-based Emotion Recognition Problem, which reflects one of the most essential points in the sphere of human-machine communications. A number of multilingual corpora (German, English and Japanese) were involved in the experiments. According to the results obtained, a high level of emotion recognition was achieved (up to a 12.97% relative improvement compared with the best F-score value on the full set of attributes).
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2016, 6, 4; 243-253
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Necessary optimality conditions for a set valued fractional programming problem in terms of contingent epiderivatives
Autorzy:
Gadhi, A. N.
Idrissi, M. El.
Powiązania:
https://bibliotekanauki.pl/articles/205692.pdf
Data publikacji:
2018
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
fractional optimization
multi-objective optimization
cone-convex mapping
optimality conditions
subdifferential
Opis:
In this paper, we are concerned with a multi-objective fractional extremal programming problem. Using the concept of subdifferential of cone-convex set valued mappings, introduced by Baier and Jahn (1999), together with the convex separation principle, we give necessary optimality conditions. An example illustrating the usefulness of our results is also provided.
Źródło:
Control and Cybernetics; 2018, 47, 2; 147-156
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-objective optimization of vehicle routing problem using evolutionary algorithm with memory
Autorzy:
Podlaski, K.
Wiatrowski, G.
Powiązania:
https://bibliotekanauki.pl/articles/305266.pdf
Data publikacji:
2017
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
vehicle routing problem
time windows
evolutionary algorithms
multi-objective optimization
Opis:
The idea of a new evolutionary algorithm with memory aspect included is proposed to find multiobjective optimized solution of vehicle routing problem with time windows. This algorithm uses population of agents that individually search for optimal solutions. The agent memory incorporates the process of learning from the experience of each individual agent as well as from the experience of the population. This algorithm uses crossover operation to define agents evolution. In the paper we choose as a base the Best Cost Route Crossover (BCRC) operator. This operator is well suited for VPRTW problems. However it does not treat both of parent symmetrically what is not natural for general evolutionary processes. The part of the paper is devoted to find an extension of the BCRC operator in order to improve inheritance of chromosomes from both of parents. Thus, the proposed evolutionary algorithm is implemented with use of two crossover operators: BCRC and its extended-modified version. We analyze the results obtained from both versions applied to Solomon’s and Gehring & Homberger instances. We conclude that the proposed method with modified version of BCRC operator gives statistically better results than those obtained using original BCRC. It seems that evolutionary algorithm with memory and modification of Best Cost Route Crossover Operator lead to very promising results when compared to the ones presented in the literature.
Źródło:
Computer Science; 2017, 18 (3); 269-286
1508-2806
2300-7036
Pojawia się w:
Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
FACTS location and size for reactive power system compensation through the multi-objective optimization
Autorzy:
Belazzoug, M.
Boudour, M.
Sebaa, K.
Powiązania:
https://bibliotekanauki.pl/articles/229744.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
reactive dispatch
multi-objective optimization
NSGA-II
SVC
TCSC
FACTS
Opis:
The problem of the FACTS (Flexible Alternative Current Transmission System Devices) location and size for reactive power system compensation through the multi-objective optimization is presented in this paper. A new technique is proposed for the optimal setting, dimension and design of two kinds of FACTS namely: Static Volt Ampere reactive (VAR) Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) handling the minimization of transmission losses in electrical network. Using the proposed scheme, the type, the location and the rating of FACTS devices are optimized simultaneously. The problem to solve is multi criteria under constraints related to the load flow equations, the voltages, the transformer turn ratios, the active and reactive productions and the compensation devices. Its solution requires the the advanced algorithms to be applied. Thus, we propose an approach based on the evolutionary algorithms (EA) to solve multi-criterion problem. It is similar to the NSGA-II method (Ellitist Non Dominated Sorting Genetic Algorithm). The Pareto front is obtained for continuous, discrete and multiple of five MVArs (Mega Volt Ampere reactive) of compensator devices for the IEEE 57-bus test system (IEEE bus test is a standard network).
Źródło:
Archives of Control Sciences; 2010, 20, 4; 473-489
1230-2384
Pojawia się w:
Archives of Control Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bi-objective routing in a dynamic network: An application to maritime logistics
Autorzy:
Maskooki, Alaleh
Nikulin, Yury
Powiązania:
https://bibliotekanauki.pl/articles/2050033.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
travelling salesman
time dependent network
multi-objective optimization
integer programming
Opis:
A bi-objectiveMILP model for optimal routing in a dynamic network with moving targets (nodes) is developed, where all targets are not necessarily visited. Hence, our problem extends the moving target travelling salesman problem. The two objectives aim at finding the sequence of targets visited in a given time horizon by minimizing the total travel distance and maximizing the number of targets visited. Due to a huge number of binary variables, such a problem often becomes intractable in the real life cases. To reduce the computational burden, we introduce a measure of traffic density, based on which we propose a time horizon splitting heuristics. In a real-world case study of greenhouse gas emissions control, using Automatic Identification System data related to the locations of ships navigating in the Gulf of Finland, we evaluate the performance of the proposed method. Different splitting scenarios are analysed numerically. Even in the cases of a moderate scale, the results show that near-efficient values for the two objectives can be obtained by our splitting approach with a drastic decrease in computational time compared to the exact MILP method. A linear value function is introduced to compare the Pareto solutions obtained by different splitting scenarios. Given our results, we expect that the present study is valuable in logistic applications, specifically maritime management services and autonomous navigation.
Źródło:
Control and Cybernetics; 2020, 49, 2; 211--232
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Wyświetlanie 1-15 z 55

Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies