Enhancing brain storm optimization through optimum-path forest
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SACI.2018.8440918 http://hdl.handle.net/11449/180186 |
Resumo: | Among the many interesting meta-heuristic optimization algorithms, one can find those inspired by both the swarm and social behavior of human beings. The Brain Storm Optimization (BSO) is motivated by the brainstorming process performed by human beings to find solutions and solve problems. Such process involves clustering the possible solutions, which can be sensitive to the number of groupings and the clustering technique itself. This work proposes a modification in the BSO working mechanism using the Optimum-Path Forest (OPF) algorithm, which does not require the knowledge about the number of clusters beforehand. Such skill is pretty much relevant when this information is unknown and must be set. The proposed approach is evaluated in a set of six benchmarking functions and showed promising results, outperforming the traditional BSO and a second variant makes use of the well-known Self-Organizing Maps clustering technique. |
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Repositório Institucional da UNESP |
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Enhancing brain storm optimization through optimum-path forestBrain Storm OptimizationClusteringMeta-heuristicsOptimum-Path ForestAmong the many interesting meta-heuristic optimization algorithms, one can find those inspired by both the swarm and social behavior of human beings. The Brain Storm Optimization (BSO) is motivated by the brainstorming process performed by human beings to find solutions and solve problems. Such process involves clustering the possible solutions, which can be sensitive to the number of groupings and the clustering technique itself. This work proposes a modification in the BSO working mechanism using the Optimum-Path Forest (OPF) algorithm, which does not require the knowledge about the number of clusters beforehand. Such skill is pretty much relevant when this information is unknown and must be set. The proposed approach is evaluated in a set of six benchmarking functions and showed promising results, outperforming the traditional BSO and a second variant makes use of the well-known Self-Organizing Maps clustering technique.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UFSCar-Federal University of Sao Carlos Department of ComputingSchool of Sciences UNESP - São Paulo State UniversitySchool of Sciences UNESP - São Paulo State UniversityFAPESP: 2013/07375-0FAPESP: 2014/12236-1FAPESP: 2014/16250-9FAPESP: 2016/06441-7CNPq: 306166/2014-3CNPq: 307066/2017-7Universidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Afonso, Luis Claudiosugi SugiPassos, LeandroPaulopapa, Joao Paulo [UNESP]2018-12-11T17:38:32Z2018-12-11T17:38:32Z2018-08-20info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject183-188http://dx.doi.org/10.1109/SACI.2018.8440918SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 183-188.http://hdl.handle.net/11449/18018610.1109/SACI.2018.84409182-s2.0-85053422902Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengSACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedingsinfo:eu-repo/semantics/openAccess2024-04-23T16:11:33Zoai:repositorio.unesp.br:11449/180186Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:33:50.129096Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Enhancing brain storm optimization through optimum-path forest |
title |
Enhancing brain storm optimization through optimum-path forest |
spellingShingle |
Enhancing brain storm optimization through optimum-path forest Afonso, Luis Claudiosugi Sugi Brain Storm Optimization Clustering Meta-heuristics Optimum-Path Forest |
title_short |
Enhancing brain storm optimization through optimum-path forest |
title_full |
Enhancing brain storm optimization through optimum-path forest |
title_fullStr |
Enhancing brain storm optimization through optimum-path forest |
title_full_unstemmed |
Enhancing brain storm optimization through optimum-path forest |
title_sort |
Enhancing brain storm optimization through optimum-path forest |
author |
Afonso, Luis Claudiosugi Sugi |
author_facet |
Afonso, Luis Claudiosugi Sugi Passos, Leandro Paulopapa, Joao Paulo [UNESP] |
author_role |
author |
author2 |
Passos, Leandro Paulopapa, Joao Paulo [UNESP] |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Afonso, Luis Claudiosugi Sugi Passos, Leandro Paulopapa, Joao Paulo [UNESP] |
dc.subject.por.fl_str_mv |
Brain Storm Optimization Clustering Meta-heuristics Optimum-Path Forest |
topic |
Brain Storm Optimization Clustering Meta-heuristics Optimum-Path Forest |
description |
Among the many interesting meta-heuristic optimization algorithms, one can find those inspired by both the swarm and social behavior of human beings. The Brain Storm Optimization (BSO) is motivated by the brainstorming process performed by human beings to find solutions and solve problems. Such process involves clustering the possible solutions, which can be sensitive to the number of groupings and the clustering technique itself. This work proposes a modification in the BSO working mechanism using the Optimum-Path Forest (OPF) algorithm, which does not require the knowledge about the number of clusters beforehand. Such skill is pretty much relevant when this information is unknown and must be set. The proposed approach is evaluated in a set of six benchmarking functions and showed promising results, outperforming the traditional BSO and a second variant makes use of the well-known Self-Organizing Maps clustering technique. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T17:38:32Z 2018-12-11T17:38:32Z 2018-08-20 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/SACI.2018.8440918 SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 183-188. http://hdl.handle.net/11449/180186 10.1109/SACI.2018.8440918 2-s2.0-85053422902 |
url |
http://dx.doi.org/10.1109/SACI.2018.8440918 http://hdl.handle.net/11449/180186 |
identifier_str_mv |
SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings, p. 183-188. 10.1109/SACI.2018.8440918 2-s2.0-85053422902 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
183-188 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129336281137152 |