Development of a Bio-inspired Hybrid Decomposition Algorithm Based on Whale and Differential Evolution Strategies for Multiobjective Optimization
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
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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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 |