Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/39470 |
Resumo: | The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems. |
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Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithmArtificial fish swarmHeuristic search0-1 knapsack problemMultidimensional knapsackEngenharia e Tecnologia::Outras Engenharias e TecnologiasThe artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.The authors wish to thank three anonymous referees for their comments and valuable suggestions to improve the paper. The first author acknowledges Ciˆencia 2007 of FCT (Foundation for Science and Technology) Portugal for the fellowship grant C2007-UMINHO-ALGORITMI-04. Financial support from FEDER COMPETE (Operational Programme Thematic Factors of Competitiveness) and FCT under project FCOMP-01-0124-FEDER-022674 is also acknowledged.Botanical Society of America Inc.Universidade do MinhoAzad, Md. Abul KalamRocha, Ana Maria A. C.Fernandes, Edite Manuela da G. P.20152015-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/39470eng2214-248710.1007/s10852-015-9275-2info: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:RCAAP2023-07-21T12:19:53Zoai:repositorium.sdum.uminho.pt:1822/39470Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:12:55.957585Repositório 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 |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
title |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
spellingShingle |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm Azad, Md. Abul Kalam Artificial fish swarm Heuristic search 0-1 knapsack problem Multidimensional knapsack Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
title_short |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
title_full |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
title_fullStr |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
title_full_unstemmed |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
title_sort |
Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm |
author |
Azad, Md. Abul Kalam |
author_facet |
Azad, Md. Abul Kalam Rocha, Ana Maria A. C. Fernandes, Edite Manuela da G. P. |
author_role |
author |
author2 |
Rocha, Ana Maria A. C. Fernandes, Edite Manuela da G. P. |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Azad, Md. Abul Kalam Rocha, Ana Maria A. C. Fernandes, Edite Manuela da G. P. |
dc.subject.por.fl_str_mv |
Artificial fish swarm Heuristic search 0-1 knapsack problem Multidimensional knapsack Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
topic |
Artificial fish swarm Heuristic search 0-1 knapsack problem Multidimensional knapsack Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
description |
The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015 2015-01-01T00:00:00Z |
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 |
http://hdl.handle.net/1822/39470 |
url |
http://hdl.handle.net/1822/39470 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2214-2487 10.1007/s10852-015-9275-2 |
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 |
Botanical Society of America Inc. |
publisher.none.fl_str_mv |
Botanical Society of America Inc. |
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 |
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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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1799132565907439616 |