Biased random-key genetic algorithm with local search applied to the maximum diversity problem

Detalhes bibliográficos
Autor(a) principal: Silva, Geiza
Data de Publicação: 2023
Outros Autores: Leite, André, Ospina, Raydonal, Leiva, Víctor, Figueroa-Zúñiga, Jorge, Castro, Cecília
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/86364
Resumo: The maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computa tional time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to tackle the MDP. Our computational study utilizes a com prehensive set of MDPLib instances, and demonstrates the superior average performance of our proposed algorithm compared to existing literature results. The MDP has a wide range of practical applications, including biology, ecology, and management. We provide future research directions for improving the algorithm’s performance and exploring its applicability in real-world scenarios.
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spelling Biased random-key genetic algorithm with local search applied to the maximum diversity problemBiological diversity conservationRandom-key genetic algorithmEvolutionary algorithmsComputational simulationsCiências Naturais::MatemáticasEducação de qualidadeThe maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computa tional time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to tackle the MDP. Our computational study utilizes a com prehensive set of MDPLib instances, and demonstrates the superior average performance of our proposed algorithm compared to existing literature results. The MDP has a wide range of practical applications, including biology, ecology, and management. We provide future research directions for improving the algorithm’s performance and exploring its applicability in real-world scenarios.This research was partially supported by the National Council for Scientific and Technological Development (CNPq) through grant 303192/2022-4 (R.O.), and Comissão de Aperfeiçoamento de Pessoal do Nível Superior (CAPES), from the Brazilian government; by FONDECYT, grant number 1200525 (V.L.), from the National Agency for Research and Development (ANID) of the Chilean government under the Ministry of Science and Technology, Knowledge, and Innovation; and by Portuguese funds through the CMAT - Research Centre of Mathematics of University of Minho, references UIDB/00013/2020, UIDP/00013/2020 (C.C.).Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoSilva, GeizaLeite, AndréOspina, RaydonalLeiva, VíctorFigueroa-Zúñiga, JorgeCastro, Cecília2023-07-122023-07-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/86364engSilva, G.; Leite, A.; Ospina, R.; Leiva, V.; Figueroa-Zúñiga, J.; Castro, C. Biased Random-Key Genetic Algorithm with Local Search Applied to the Maximum Diversity Problem. Mathematics 2023, 11, 3072. https://doi.org/10.3390/math111430722227-739010.3390/math111430723072https://www.mdpi.com/2227-7390/11/14/3072info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-10-21T01:26:12Zoai:repositorium.sdum.uminho.pt:1822/86364Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:29:22.687626Repositório 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 Biased random-key genetic algorithm with local search applied to the maximum diversity problem
title Biased random-key genetic algorithm with local search applied to the maximum diversity problem
spellingShingle Biased random-key genetic algorithm with local search applied to the maximum diversity problem
Silva, Geiza
Biological diversity conservation
Random-key genetic algorithm
Evolutionary algorithms
Computational simulations
Ciências Naturais::Matemáticas
Educação de qualidade
title_short Biased random-key genetic algorithm with local search applied to the maximum diversity problem
title_full Biased random-key genetic algorithm with local search applied to the maximum diversity problem
title_fullStr Biased random-key genetic algorithm with local search applied to the maximum diversity problem
title_full_unstemmed Biased random-key genetic algorithm with local search applied to the maximum diversity problem
title_sort Biased random-key genetic algorithm with local search applied to the maximum diversity problem
author Silva, Geiza
author_facet Silva, Geiza
Leite, André
Ospina, Raydonal
Leiva, Víctor
Figueroa-Zúñiga, Jorge
Castro, Cecília
author_role author
author2 Leite, André
Ospina, Raydonal
Leiva, Víctor
Figueroa-Zúñiga, Jorge
Castro, Cecília
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Silva, Geiza
Leite, André
Ospina, Raydonal
Leiva, Víctor
Figueroa-Zúñiga, Jorge
Castro, Cecília
dc.subject.por.fl_str_mv Biological diversity conservation
Random-key genetic algorithm
Evolutionary algorithms
Computational simulations
Ciências Naturais::Matemáticas
Educação de qualidade
topic Biological diversity conservation
Random-key genetic algorithm
Evolutionary algorithms
Computational simulations
Ciências Naturais::Matemáticas
Educação de qualidade
description The maximum diversity problem (MDP) aims to select a subset with a predetermined number of elements from a given set, maximizing the diversity among them. This NP-hard problem requires efficient algorithms that can generate high-quality solutions within reasonable computa tional time. In this study, we propose a novel approach that combines the biased random-key genetic algorithm (BRKGA) with local search to tackle the MDP. Our computational study utilizes a com prehensive set of MDPLib instances, and demonstrates the superior average performance of our proposed algorithm compared to existing literature results. The MDP has a wide range of practical applications, including biology, ecology, and management. We provide future research directions for improving the algorithm’s performance and exploring its applicability in real-world scenarios.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-12
2023-07-12T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/86364
url https://hdl.handle.net/1822/86364
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Silva, G.; Leite, A.; Ospina, R.; Leiva, V.; Figueroa-Zúñiga, J.; Castro, C. Biased Random-Key Genetic Algorithm with Local Search Applied to the Maximum Diversity Problem. Mathematics 2023, 11, 3072. https://doi.org/10.3390/math11143072
2227-7390
10.3390/math11143072
3072
https://www.mdpi.com/2227-7390/11/14/3072
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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