Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization

Detalhes bibliográficos
Autor(a) principal: Martins, André O.
Data de Publicação: 2023
Outros Autores: Peito, Marcela C. C., Vargas, Dênis E. C., Wanner, Elizabeth F.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Vetor (Online)
Texto Completo: https://periodicos.furg.br/vetor/article/view/15567
Resumo: A Multiobjective Optimization Problem (MOP) requires the optimization of several objective functions simultaneously, usually in conflict with each other. One of the most efficient algorithms for solving MOPs is MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), which decomposes a MOP into single-objective optimization subproblems and solves them using information from neighboring subproblems. MOEA/D variants with other evolutionary operators have emerged over the years, improving their efficiency in various MOPs. Recently, the IWOA (Improved Whale Optimization Algorithm) was proposed, an optimization algorithm bioinspired by the whale hunting method hybridized with Differential Evolution, which presented excellent results in single-objective optimization problems. This work proposes the MOEA/D-IWOA algorithm, which associates characteristics of the evolutionary operators of the IWOA to MOEA/D. Computational experiments were accomplished to analyze the performance of the MOEA/D-IWOA in benchmark MOPs suites. The results were compared with those obtained by the MOEA/D, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Third Evolution Step of Generalized Differential Evolution (GDE3), Improving the Strength Pareto Evolutionary Algorithm (SPEA2), and Indicator-Based Evolutionary Algorithm (IBEA) algorithms in the Hypervolume and Inverted Generational Distance Plus (IGD+) indicators. The MOEA/D-IWOA proved to be competitive, with a good performance profile, in addition to presenting the best results in some POMs.
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spelling Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective OptimizationDesenvolvimento de um Algoritmo de Decomposição Híbrido Bioinspirado Baseado em Baleias e Estratégias de Evolução Diferencial para Otimização MultiobjetivoMultiobjective OptimizationMOEA/DIWOAMulti-objective OptimizationOtimização multiobjetivoMOEA/DIWOAA Multiobjective Optimization Problem (MOP) requires the optimization of several objective functions simultaneously, usually in conflict with each other. One of the most efficient algorithms for solving MOPs is MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), which decomposes a MOP into single-objective optimization subproblems and solves them using information from neighboring subproblems. MOEA/D variants with other evolutionary operators have emerged over the years, improving their efficiency in various MOPs. Recently, the IWOA (Improved Whale Optimization Algorithm) was proposed, an optimization algorithm bioinspired by the whale hunting method hybridized with Differential Evolution, which presented excellent results in single-objective optimization problems. This work proposes the MOEA/D-IWOA algorithm, which associates characteristics of the evolutionary operators of the IWOA to MOEA/D. Computational experiments were accomplished to analyze the performance of the MOEA/D-IWOA in benchmark MOPs suites. The results were compared with those obtained by the MOEA/D, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Third Evolution Step of Generalized Differential Evolution (GDE3), Improving the Strength Pareto Evolutionary Algorithm (SPEA2), and Indicator-Based Evolutionary Algorithm (IBEA) algorithms in the Hypervolume and Inverted Generational Distance Plus (IGD+) indicators. The MOEA/D-IWOA proved to be competitive, with a good performance profile, in addition to presenting the best results in some POMs.Um Problema de Otimização Multiobjetivo (POM) requer a otimização de várias funções objetivo simultaneamente, geralmente conflitantes entre si. Um dos algoritmos mais eficientes para resolver POMs é o MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), que decompõe um POM em subproblemas de otimização monobjetivo, isto é, com uma única função objetivo a ser minimizada, e os resolve usando informações de subproblemas vizinhos. Variantes do MOEA/D com outros operadores evolutivos surgiram ao longo dos anos, melhorando sua eficiência em diversos POMs. Recentemente foi proposto o IWOA (Improved Whale Optimization Algorithm), um algoritmo de otimização bioinspirado no método de caça das baleias hibridizado com Evolução Diferencial que apresentou ótimos resultados em problemas de otimização monobjetivo. Esse trabalho propõe o algoritmo MOEA/D-IWOA, que extende o IWOA para resolver POMs associando características dos seus operadores evolutivos ao MOEA/D. Experimentos computacionais para analisar o desempenho do MOEA/D-IWOA em POMs benchmark foram realizados e os resultados comparados aos obtidos pelos algoritmos bem conhecidos da literatura, a saber, MOEA/D, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Third Evolution Step of Generalized Differential Evolution (GDE3), Improving the Strength Pareto Evolutionary Algorithm (SPEA2) e Indicator-Based Evolutionary Algorithm (IBEA) nos indicadores Hypervolume e Inverted Generational Distance Plus (IGD+). O MOEA/D-IWOA se mostrou competitivo, com bom perfil de desempenho, além de apresentar os melhores resultados em alguns POMs.Universidade Federal do Rio Grande2023-06-28info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://periodicos.furg.br/vetor/article/view/1556710.14295/vetor.v33i1.15567VETOR - Journal of Exact Sciences and Engineering; Vol. 33 No. 1 (2023); 13-24VETOR - Revista de Ciências Exatas e Engenharias; v. 33 n. 1 (2023); 13-242358-34520102-7352reponame:Vetor (Online)instname:Universidade Federal do Rio Grande (FURG)instacron:FURGenghttps://periodicos.furg.br/vetor/article/view/15567/10192Copyright (c) 2023 VETOR - Revista de Ciências Exatas e Engenhariasinfo:eu-repo/semantics/openAccessMartins, André O.Peito, Marcela C. C.Vargas, Dênis E. C.Wanner, Elizabeth F.2023-06-28T19:45:10Zoai:ojs.periodicos.furg.br:article/15567Revistahttps://periodicos.furg.br/vetorPUBhttps://periodicos.furg.br/vetor/oaigmplatt@furg.br2358-34520102-7352opendoar:2023-06-28T19:45:10Vetor (Online) - Universidade Federal do Rio Grande (FURG)false
dc.title.none.fl_str_mv Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
Desenvolvimento de um Algoritmo de Decomposição Híbrido Bioinspirado Baseado em Baleias e Estratégias de Evolução Diferencial para Otimização Multiobjetivo
title Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
spellingShingle Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
Martins, André O.
Multiobjective Optimization
MOEA/D
IWOA
Multi-objective Optimization
Otimização multiobjetivo
MOEA/D
IWOA
title_short Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
title_full Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
title_fullStr Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
title_full_unstemmed Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
title_sort Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
author Martins, André O.
author_facet Martins, André O.
Peito, Marcela C. C.
Vargas, Dênis E. C.
Wanner, Elizabeth F.
author_role author
author2 Peito, Marcela C. C.
Vargas, Dênis E. C.
Wanner, Elizabeth F.
author2_role author
author
author
dc.contributor.author.fl_str_mv Martins, André O.
Peito, Marcela C. C.
Vargas, Dênis E. C.
Wanner, Elizabeth F.
dc.subject.por.fl_str_mv Multiobjective Optimization
MOEA/D
IWOA
Multi-objective Optimization
Otimização multiobjetivo
MOEA/D
IWOA
topic Multiobjective Optimization
MOEA/D
IWOA
Multi-objective Optimization
Otimização multiobjetivo
MOEA/D
IWOA
description A Multiobjective Optimization Problem (MOP) requires the optimization of several objective functions simultaneously, usually in conflict with each other. One of the most efficient algorithms for solving MOPs is MOEA/D (Multiobjective Evolutionary Algorithm Based on Decomposition), which decomposes a MOP into single-objective optimization subproblems and solves them using information from neighboring subproblems. MOEA/D variants with other evolutionary operators have emerged over the years, improving their efficiency in various MOPs. Recently, the IWOA (Improved Whale Optimization Algorithm) was proposed, an optimization algorithm bioinspired by the whale hunting method hybridized with Differential Evolution, which presented excellent results in single-objective optimization problems. This work proposes the MOEA/D-IWOA algorithm, which associates characteristics of the evolutionary operators of the IWOA to MOEA/D. Computational experiments were accomplished to analyze the performance of the MOEA/D-IWOA in benchmark MOPs suites. The results were compared with those obtained by the MOEA/D, Non-dominated Sorting Genetic Algorithm II (NSGA-II), Third Evolution Step of Generalized Differential Evolution (GDE3), Improving the Strength Pareto Evolutionary Algorithm (SPEA2), and Indicator-Based Evolutionary Algorithm (IBEA) algorithms in the Hypervolume and Inverted Generational Distance Plus (IGD+) indicators. The MOEA/D-IWOA proved to be competitive, with a good performance profile, in addition to presenting the best results in some POMs.
publishDate 2023
dc.date.none.fl_str_mv 2023-06-28
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.furg.br/vetor/article/view/15567
10.14295/vetor.v33i1.15567
url https://periodicos.furg.br/vetor/article/view/15567
identifier_str_mv 10.14295/vetor.v33i1.15567
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://periodicos.furg.br/vetor/article/view/15567/10192
dc.rights.driver.fl_str_mv Copyright (c) 2023 VETOR - Revista de Ciências Exatas e Engenharias
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 VETOR - Revista de Ciências Exatas e Engenharias
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade Federal do Rio Grande
publisher.none.fl_str_mv Universidade Federal do Rio Grande
dc.source.none.fl_str_mv VETOR - Journal of Exact Sciences and Engineering; Vol. 33 No. 1 (2023); 13-24
VETOR - Revista de Ciências Exatas e Engenharias; v. 33 n. 1 (2023); 13-24
2358-3452
0102-7352
reponame:Vetor (Online)
instname:Universidade Federal do Rio Grande (FURG)
instacron:FURG
instname_str Universidade Federal do Rio Grande (FURG)
instacron_str FURG
institution FURG
reponame_str Vetor (Online)
collection Vetor (Online)
repository.name.fl_str_mv Vetor (Online) - Universidade Federal do Rio Grande (FURG)
repository.mail.fl_str_mv gmplatt@furg.br
_version_ 1797041760282607616

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