Biased random-key genetic algorithm with local search applied to the maximum diversity problem
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , , , |
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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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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1799133561810321408 |