Quaternion-Based Backtracking Search Optimization Algorithm
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/197587 |
Resumo: | Fitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based Backtracking Search Optimization Algorithm, a variant of the standard Backtracking Search Optimization Algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well. |
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Repositório Institucional da UNESP |
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Quaternion-Based Backtracking Search Optimization AlgorithmBacktracking Search Optimization AlgorithmQuaternionsMeta-heuristicsFitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based Backtracking Search Optimization Algorithm, a variant of the standard Backtracking Search Optimization Algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)UFSCar Fed Univ Sao Carlos, Dept Comp, Sao Carlos, SP, BrazilUNESP Sao Paulo State Univ, Sch Sci, Bauru, SP, BrazilUNESP Sao Paulo State Univ, Sch Sci, Bauru, SP, BrazilCAPES: 001FAPESP: 2013/07375-0FAPESP: 2014/16250-9FAPESP: 2014/12236-1FAPESP: 2016/06441-7CNPq: 306166/2014-3CNPq: 307066/2017-7IeeeUniversidade Federal de São Carlos (UFSCar)Universidade Estadual Paulista (Unesp)Passos, Leandro AparecidoRodrigues, DouglasPapa, Joao Paulo [UNESP]IEEE2020-12-11T04:54:41Z2020-12-11T04:54:41Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject3014-30212019 Ieee Congress On Evolutionary Computation (cec). New York: Ieee, p. 3014-3021, 2019.http://hdl.handle.net/11449/197587WOS:000502087103005Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2019 Ieee Congress On Evolutionary Computation (cec)info:eu-repo/semantics/openAccess2024-04-23T16:11:34Zoai:repositorio.unesp.br:11449/197587Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-04-23T16:11:34Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Quaternion-Based Backtracking Search Optimization Algorithm |
title |
Quaternion-Based Backtracking Search Optimization Algorithm |
spellingShingle |
Quaternion-Based Backtracking Search Optimization Algorithm Passos, Leandro Aparecido Backtracking Search Optimization Algorithm Quaternions Meta-heuristics |
title_short |
Quaternion-Based Backtracking Search Optimization Algorithm |
title_full |
Quaternion-Based Backtracking Search Optimization Algorithm |
title_fullStr |
Quaternion-Based Backtracking Search Optimization Algorithm |
title_full_unstemmed |
Quaternion-Based Backtracking Search Optimization Algorithm |
title_sort |
Quaternion-Based Backtracking Search Optimization Algorithm |
author |
Passos, Leandro Aparecido |
author_facet |
Passos, Leandro Aparecido Rodrigues, Douglas Papa, Joao Paulo [UNESP] IEEE |
author_role |
author |
author2 |
Rodrigues, Douglas Papa, Joao Paulo [UNESP] IEEE |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de São Carlos (UFSCar) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Passos, Leandro Aparecido Rodrigues, Douglas Papa, Joao Paulo [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Backtracking Search Optimization Algorithm Quaternions Meta-heuristics |
topic |
Backtracking Search Optimization Algorithm Quaternions Meta-heuristics |
description |
Fitness landscape has been one of the main limitations regarding optimization tasks. Although meta-heuristic techniques have achieved outstanding results over a large variety of problems, some issues related to the function geometry and the risk to get trapped from local optima are issues that still require attention. To deal with this problem, we propose the Quaternion-based Backtracking Search Optimization Algorithm, a variant of the standard Backtracking Search Optimization Algorithm that maps each decision variable in a tensor onto a hypercomplex search space, whose landscape is expected to be smoother. Experiments conducted using nine benchmarking functions showed considerably better results than the ones achieved over standard search spaces, as well as more accurate results than some quaternion-based methods as well. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2020-12-11T04:54:41Z 2020-12-11T04:54:41Z |
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 |
2019 Ieee Congress On Evolutionary Computation (cec). New York: Ieee, p. 3014-3021, 2019. http://hdl.handle.net/11449/197587 WOS:000502087103005 |
identifier_str_mv |
2019 Ieee Congress On Evolutionary Computation (cec). New York: Ieee, p. 3014-3021, 2019. WOS:000502087103005 |
url |
http://hdl.handle.net/11449/197587 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2019 Ieee Congress On Evolutionary Computation (cec) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
3014-3021 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science 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_ |
1799965694805147648 |