Algoritmo híbrido multiobjetivo para o problema flexible job shop
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFU |
Texto Completo: | https://repositorio.ufu.br/handle/123456789/12583 https://doi.org/10.14393/ufu.di.2015.3 |
Resumo: | Flexible Job Shop (FJSP) is an important combinatorial optimization problem that has been widely researched by means evolutionary computation techniques. Algorithms based on Particle Swarm Optimization (PSO), for example, have shown good results for this problem, but tend to converge prematurely. Moreover, Genetic Algorithms (GAs) have the ability to deal with a large search space, but do not guarantee convergence. The multiobjective aspect of this problem has been considered by both of these techniques. These features have inspired this work, which presents a hybrid and multi-objective algorithm based on PSO, genetic operators and Pareto optimal settings through the procedure Fast Non-dominated Sorting (FNS). While the PSO characteristics guarantee convergence, genetic operators improve the exploitation of the search space. However, in order to reach better results than those presented in the literature, two significant innovations were introduced and a new PSO algorithm was obtained, that it is denominated PSO using Diversity (DIPSO), which is efficient to cope with FJSP. The first innovation was to take the global best as the Front-1 produced by Pareto optimal. Then, the global best is not just an individual of the population as in the original PSO, but a set containing all the best solutions found during the algorithm execution. The second innovation was the development of a crossover operator, which introduced diversity in the population and allowed to expand exploration of the search space. These two innovations allowed to find new solutions in three of the fifteen FJSP instances, improve results previously reported in the literature and reproduce several known solutions, demonstrating the DIPSO efficiency in addressing FJSP. It has been also observed that DIPSO is a new proposal of evolutionary algorithms, which can be used in other problems of this nature. In addition to the approach described in DIPSO, other proposals were investigated, such as the introduction of new objectives for the FJSP and the use of new techniques not yet explored. |
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Algoritmo híbrido multiobjetivo para o problema flexible job shopAlgoritmos evolutivosFlexible job shop problemParticle swarm optimizationAlgoritmo genéticoNon-dominated sorting genetic algorithmAlgoritmo evolutivo multiobjetivo em tabelasComputação evolutivaFlexible Job ShopOtimização combinatóriaEvolutionary algorithmsGenetic algorithmMulti-objective evolutionary algorithms with many tablesCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOFlexible Job Shop (FJSP) is an important combinatorial optimization problem that has been widely researched by means evolutionary computation techniques. Algorithms based on Particle Swarm Optimization (PSO), for example, have shown good results for this problem, but tend to converge prematurely. Moreover, Genetic Algorithms (GAs) have the ability to deal with a large search space, but do not guarantee convergence. The multiobjective aspect of this problem has been considered by both of these techniques. These features have inspired this work, which presents a hybrid and multi-objective algorithm based on PSO, genetic operators and Pareto optimal settings through the procedure Fast Non-dominated Sorting (FNS). While the PSO characteristics guarantee convergence, genetic operators improve the exploitation of the search space. However, in order to reach better results than those presented in the literature, two significant innovations were introduced and a new PSO algorithm was obtained, that it is denominated PSO using Diversity (DIPSO), which is efficient to cope with FJSP. The first innovation was to take the global best as the Front-1 produced by Pareto optimal. Then, the global best is not just an individual of the population as in the original PSO, but a set containing all the best solutions found during the algorithm execution. The second innovation was the development of a crossover operator, which introduced diversity in the population and allowed to expand exploration of the search space. These two innovations allowed to find new solutions in three of the fifteen FJSP instances, improve results previously reported in the literature and reproduce several known solutions, demonstrating the DIPSO efficiency in addressing FJSP. It has been also observed that DIPSO is a new proposal of evolutionary algorithms, which can be used in other problems of this nature. In addition to the approach described in DIPSO, other proposals were investigated, such as the introduction of new objectives for the FJSP and the use of new techniques not yet explored.Mestre em Ciência da ComputaçãoFlexible Job Shop (FJSP) é um importante problema de otimização combinatorial, que tem sido largamente pesquisado através de técnicas da Computação Evolutiva. Algoritmos baseados em Particle Swarm Optimization (PSO), por exemplo, têm apresentado bons resultados para esse problema, porém tendem a convergir prematuramente. Por outro lado, Algoritmos Genéticos (AGs) têm a capacidade de tratar um amplo espaço de busca, mas não garantem a convergência. O aspecto multiobjetivo desse problema tem sido tratado por ambas as técnicas. Essas características inspiraram este trabalho, que apresenta um algoritmo híbrido e multiobjetivo, baseado em PSO, operadores genéticos e definições do ótimo de Pareto por meio do procedimento Fast Non-dominated Sorting (FNS). Enquanto as características do PSO garantem a convergência, os operadores genéticos melhoram a exploração do espaço de busca. Contudo, com o objetivo de obter melhores resultados que aqueles apresentados na literatura, duas significantes inovações foram introduzidas, produzindo um novo algoritmo PSO, denominado PSO com Diversidade (DIPSO), que se mostrou eficiente para tratar o FJSP. A primeira inovação foi tomar o melhor global como o Front-1 produzido pelo ótimo de Pareto. Assim, o melhor global não é apenas um indivíduo da população como na proposta original do PSO, mas um conjunto contendo todas as melhores soluções encontradas durante a execução do algoritmo. A segunda inovação foi a criação de um operador de cruzamento, que introduziu diversidade na população e permitiu ampliar a exploração do espaço de busca. Essas duas inovações permitiram encontrar novas soluções em três das quinze instâncias do FJSP, melhorar resultados anteriormente apresentados na literatura e reproduzir diversas soluções conhecidas, demonstrando a eficiência de DIPSO para tratar o FJSP. Observa-se ainda que DIPSO é uma nova proposta para algoritmos da área de Computação Evolutiva, que pode ser utilizada em outros problemas dessa natureza. Além da abordagem descrita em DIPSO, outras propostas foram investigadas, tais como a introdução de novos objetivos para tratar o FJSP e a utilização de novas técnicas ainda não exploradas.Universidade Federal de UberlândiaBRPrograma de Pós-graduação em Ciência da ComputaçãoCiências Exatas e da TerraUFUFernandes, Márcia Aparecidahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4782141Z6Santos, Anderson Rodrigues doshttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4211637U0Soares, Anderson da Silvahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4732642H1Carvalho, Luiz Carlos Felix2016-06-22T18:32:33Z2015-05-152016-06-22T18:32:33Z2015-01-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/pdfCARVALHO, Luiz Carlos Felix. Algoritmo híbrido multiobjetivo para o problema flexible job shop. 2015. 159 f. Dissertação (Mestrado em Ciências Exatas e da Terra) - Universidade Federal de Uberlândia, Uberlândia, 2015. DOI https://doi.org/10.14393/ufu.di.2015.3https://repositorio.ufu.br/handle/123456789/12583https://doi.org/10.14393/ufu.di.2015.3porinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFUinstname:Universidade Federal de Uberlândia (UFU)instacron:UFU2021-03-15T17:11:47Zoai:repositorio.ufu.br:123456789/12583Repositório InstitucionalONGhttp://repositorio.ufu.br/oai/requestdiinf@dirbi.ufu.bropendoar:2021-03-15T17:11:47Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU)false |
dc.title.none.fl_str_mv |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
title |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
spellingShingle |
Algoritmo híbrido multiobjetivo para o problema flexible job shop Carvalho, Luiz Carlos Felix Algoritmos evolutivos Flexible job shop problem Particle swarm optimization Algoritmo genético Non-dominated sorting genetic algorithm Algoritmo evolutivo multiobjetivo em tabelas Computação evolutiva Flexible Job Shop Otimização combinatória Evolutionary algorithms Genetic algorithm Multi-objective evolutionary algorithms with many tables CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
title_full |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
title_fullStr |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
title_full_unstemmed |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
title_sort |
Algoritmo híbrido multiobjetivo para o problema flexible job shop |
author |
Carvalho, Luiz Carlos Felix |
author_facet |
Carvalho, Luiz Carlos Felix |
author_role |
author |
dc.contributor.none.fl_str_mv |
Fernandes, Márcia Aparecida http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4782141Z6 Santos, Anderson Rodrigues dos http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4211637U0 Soares, Anderson da Silva http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4732642H1 |
dc.contributor.author.fl_str_mv |
Carvalho, Luiz Carlos Felix |
dc.subject.por.fl_str_mv |
Algoritmos evolutivos Flexible job shop problem Particle swarm optimization Algoritmo genético Non-dominated sorting genetic algorithm Algoritmo evolutivo multiobjetivo em tabelas Computação evolutiva Flexible Job Shop Otimização combinatória Evolutionary algorithms Genetic algorithm Multi-objective evolutionary algorithms with many tables CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
topic |
Algoritmos evolutivos Flexible job shop problem Particle swarm optimization Algoritmo genético Non-dominated sorting genetic algorithm Algoritmo evolutivo multiobjetivo em tabelas Computação evolutiva Flexible Job Shop Otimização combinatória Evolutionary algorithms Genetic algorithm Multi-objective evolutionary algorithms with many tables CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
Flexible Job Shop (FJSP) is an important combinatorial optimization problem that has been widely researched by means evolutionary computation techniques. Algorithms based on Particle Swarm Optimization (PSO), for example, have shown good results for this problem, but tend to converge prematurely. Moreover, Genetic Algorithms (GAs) have the ability to deal with a large search space, but do not guarantee convergence. The multiobjective aspect of this problem has been considered by both of these techniques. These features have inspired this work, which presents a hybrid and multi-objective algorithm based on PSO, genetic operators and Pareto optimal settings through the procedure Fast Non-dominated Sorting (FNS). While the PSO characteristics guarantee convergence, genetic operators improve the exploitation of the search space. However, in order to reach better results than those presented in the literature, two significant innovations were introduced and a new PSO algorithm was obtained, that it is denominated PSO using Diversity (DIPSO), which is efficient to cope with FJSP. The first innovation was to take the global best as the Front-1 produced by Pareto optimal. Then, the global best is not just an individual of the population as in the original PSO, but a set containing all the best solutions found during the algorithm execution. The second innovation was the development of a crossover operator, which introduced diversity in the population and allowed to expand exploration of the search space. These two innovations allowed to find new solutions in three of the fifteen FJSP instances, improve results previously reported in the literature and reproduce several known solutions, demonstrating the DIPSO efficiency in addressing FJSP. It has been also observed that DIPSO is a new proposal of evolutionary algorithms, which can be used in other problems of this nature. In addition to the approach described in DIPSO, other proposals were investigated, such as the introduction of new objectives for the FJSP and the use of new techniques not yet explored. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-05-15 2015-01-15 2016-06-22T18:32:33Z 2016-06-22T18:32:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
CARVALHO, Luiz Carlos Felix. Algoritmo híbrido multiobjetivo para o problema flexible job shop. 2015. 159 f. Dissertação (Mestrado em Ciências Exatas e da Terra) - Universidade Federal de Uberlândia, Uberlândia, 2015. DOI https://doi.org/10.14393/ufu.di.2015.3 https://repositorio.ufu.br/handle/123456789/12583 https://doi.org/10.14393/ufu.di.2015.3 |
identifier_str_mv |
CARVALHO, Luiz Carlos Felix. Algoritmo híbrido multiobjetivo para o problema flexible job shop. 2015. 159 f. Dissertação (Mestrado em Ciências Exatas e da Terra) - Universidade Federal de Uberlândia, Uberlândia, 2015. DOI https://doi.org/10.14393/ufu.di.2015.3 |
url |
https://repositorio.ufu.br/handle/123456789/12583 https://doi.org/10.14393/ufu.di.2015.3 |
dc.language.iso.fl_str_mv |
por |
language |
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info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Uberlândia BR Programa de Pós-graduação em Ciência da Computação Ciências Exatas e da Terra UFU |
publisher.none.fl_str_mv |
Universidade Federal de Uberlândia BR Programa de Pós-graduação em Ciência da Computação Ciências Exatas e da Terra UFU |
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reponame:Repositório Institucional da UFU instname:Universidade Federal de Uberlândia (UFU) instacron:UFU |
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Universidade Federal de Uberlândia (UFU) |
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Repositório Institucional da UFU |
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Repositório Institucional da UFU |
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Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU) |
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diinf@dirbi.ufu.br |
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1805569617307893760 |