Entropy Diversity in Multi-Objective Particle Swarm Optimization

Detalhes bibliográficos
Autor(a) principal: Eduardo Pires
Data de Publicação: 2013
Outros Autores: Tenreiro Machado,JAT, Paulo Moura Oliveira
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://repositorio.inesctec.pt/handle/123456789/5145
http://dx.doi.org/10.3390/e15125475
Resumo: Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.
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spelling Entropy Diversity in Multi-Objective Particle Swarm OptimizationMulti-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.2017-12-31T12:15:55Z2013-01-01T00:00:00Z2013info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://repositorio.inesctec.pt/handle/123456789/5145http://dx.doi.org/10.3390/e15125475engEduardo PiresTenreiro Machado,JATPaulo Moura Oliveirainfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-05-15T10:19:51Zoai:repositorio.inesctec.pt:123456789/5145Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:52:20.110882Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Entropy Diversity in Multi-Objective Particle Swarm Optimization
title Entropy Diversity in Multi-Objective Particle Swarm Optimization
spellingShingle Entropy Diversity in Multi-Objective Particle Swarm Optimization
Eduardo Pires
title_short Entropy Diversity in Multi-Objective Particle Swarm Optimization
title_full Entropy Diversity in Multi-Objective Particle Swarm Optimization
title_fullStr Entropy Diversity in Multi-Objective Particle Swarm Optimization
title_full_unstemmed Entropy Diversity in Multi-Objective Particle Swarm Optimization
title_sort Entropy Diversity in Multi-Objective Particle Swarm Optimization
author Eduardo Pires
author_facet Eduardo Pires
Tenreiro Machado,JAT
Paulo Moura Oliveira
author_role author
author2 Tenreiro Machado,JAT
Paulo Moura Oliveira
author2_role author
author
dc.contributor.author.fl_str_mv Eduardo Pires
Tenreiro Machado,JAT
Paulo Moura Oliveira
description Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.
publishDate 2013
dc.date.none.fl_str_mv 2013-01-01T00:00:00Z
2013
2017-12-31T12:15:55Z
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dc.identifier.uri.fl_str_mv http://repositorio.inesctec.pt/handle/123456789/5145
http://dx.doi.org/10.3390/e15125475
url http://repositorio.inesctec.pt/handle/123456789/5145
http://dx.doi.org/10.3390/e15125475
dc.language.iso.fl_str_mv eng
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