A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.5220/0006106402030210 http://hdl.handle.net/11449/159878 |
Resumo: | The fine-tuning of the algorithms parameters, specially, in metaheuristics, is not always trivial and often is performed by ad hoc methods according to the problem under analysis. Usually, incorrect settings influence both in the algorithms performance, as in the quality of solutions. The tuning of metaheuristics requires the use of innovative methodologies, usually interesting to different research communities. In this context, this paper aims to contribute to the literature by presenting a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters, looking for candidate configurations near of a promising alternative, and consistently finds good settings for different metaheuristics. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve a classical task scheduling problem. The results of the proposed approach are compared with results yielded by the same metaheuristics tuned through different strategies, such as the brute-force and racing. Broadly, the proposed method proved to be effective in terms of the overall time of the tuning process. Our results from experimental studies reveal that metaheuristics tuned by means of HORA reach the same good results than when tuned by the other time-consuming fine-tuning approaches. Therefore, from the results presented in this study it is concluded that HORA is a promising and powerful tool for the fine-tuning of different metaheuristics, mainly when the overall time of tuning process is considered. |
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A Heuristic for Optimization of Metaheuristics by Means of Statistical MethodsMetaheuristicsFine-tuningCombinatorial OptimizationNonparametric StatisticsThe fine-tuning of the algorithms parameters, specially, in metaheuristics, is not always trivial and often is performed by ad hoc methods according to the problem under analysis. Usually, incorrect settings influence both in the algorithms performance, as in the quality of solutions. The tuning of metaheuristics requires the use of innovative methodologies, usually interesting to different research communities. In this context, this paper aims to contribute to the literature by presenting a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters, looking for candidate configurations near of a promising alternative, and consistently finds good settings for different metaheuristics. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve a classical task scheduling problem. The results of the proposed approach are compared with results yielded by the same metaheuristics tuned through different strategies, such as the brute-force and racing. Broadly, the proposed method proved to be effective in terms of the overall time of the tuning process. Our results from experimental studies reveal that metaheuristics tuned by means of HORA reach the same good results than when tuned by the other time-consuming fine-tuning approaches. Therefore, from the results presented in this study it is concluded that HORA is a promising and powerful tool for the fine-tuning of different metaheuristics, mainly when the overall time of tuning process is considered.Brazilian Natl Inst Space Res, Rod Presidente Dutra,Km 40, BR-12630000 Sao Paulo, SP, BrazilUniv Estadual Paulista, Sch Engn Guaratingueta, Ave Dr Ariberto Pereira da Cunha 333, BR-12516410 Sao Paulo, SP, BrazilUniv Estadual Paulista, Sch Engn Guaratingueta, Ave Dr Ariberto Pereira da Cunha 333, BR-12516410 Sao Paulo, SP, BrazilScitepressBrazilian Natl Inst Space ResUniversidade Estadual Paulista (Unesp)Barbosa, Eduardo B. M.Senne, Edson L. F. [UNESP]Liberatore, F.Parlier, G. H.Demange, M.2018-11-26T15:45:35Z2018-11-26T15:45:35Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject203-210http://dx.doi.org/10.5220/0006106402030210Proceedings Of The 6th International Conference On Operations Research And Enterprise Systems (icores). Setubal: Scitepress, p. 203-210, 2017.http://hdl.handle.net/11449/15987810.5220/0006106402030210WOS:000413254200019Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings Of The 6th International Conference On Operations Research And Enterprise Systems (icores)info:eu-repo/semantics/openAccess2021-10-23T21:44:37Zoai:repositorio.unesp.br:11449/159878Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:26:39.258056Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
title |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
spellingShingle |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods Barbosa, Eduardo B. M. Metaheuristics Fine-tuning Combinatorial Optimization Nonparametric Statistics |
title_short |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
title_full |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
title_fullStr |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
title_full_unstemmed |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
title_sort |
A Heuristic for Optimization of Metaheuristics by Means of Statistical Methods |
author |
Barbosa, Eduardo B. M. |
author_facet |
Barbosa, Eduardo B. M. Senne, Edson L. F. [UNESP] Liberatore, F. Parlier, G. H. Demange, M. |
author_role |
author |
author2 |
Senne, Edson L. F. [UNESP] Liberatore, F. Parlier, G. H. Demange, M. |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Brazilian Natl Inst Space Res Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Barbosa, Eduardo B. M. Senne, Edson L. F. [UNESP] Liberatore, F. Parlier, G. H. Demange, M. |
dc.subject.por.fl_str_mv |
Metaheuristics Fine-tuning Combinatorial Optimization Nonparametric Statistics |
topic |
Metaheuristics Fine-tuning Combinatorial Optimization Nonparametric Statistics |
description |
The fine-tuning of the algorithms parameters, specially, in metaheuristics, is not always trivial and often is performed by ad hoc methods according to the problem under analysis. Usually, incorrect settings influence both in the algorithms performance, as in the quality of solutions. The tuning of metaheuristics requires the use of innovative methodologies, usually interesting to different research communities. In this context, this paper aims to contribute to the literature by presenting a methodology combining Statistical and Artificial Intelligence methods in the fine-tuning of metaheuristics. The key idea is a heuristic method, called Heuristic Oriented Racing Algorithm (HORA), which explores a search space of parameters, looking for candidate configurations near of a promising alternative, and consistently finds good settings for different metaheuristics. To confirm the validity of this approach, we present a case study for fine-tuning two distinct metaheuristics: Simulated Annealing (SA) and Genetic Algorithm (GA), in order to solve a classical task scheduling problem. The results of the proposed approach are compared with results yielded by the same metaheuristics tuned through different strategies, such as the brute-force and racing. Broadly, the proposed method proved to be effective in terms of the overall time of the tuning process. Our results from experimental studies reveal that metaheuristics tuned by means of HORA reach the same good results than when tuned by the other time-consuming fine-tuning approaches. Therefore, from the results presented in this study it is concluded that HORA is a promising and powerful tool for the fine-tuning of different metaheuristics, mainly when the overall time of tuning process is considered. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-11-26T15:45:35Z 2018-11-26T15:45:35Z |
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/10.5220/0006106402030210 Proceedings Of The 6th International Conference On Operations Research And Enterprise Systems (icores). Setubal: Scitepress, p. 203-210, 2017. http://hdl.handle.net/11449/159878 10.5220/0006106402030210 WOS:000413254200019 |
url |
http://dx.doi.org/10.5220/0006106402030210 http://hdl.handle.net/11449/159878 |
identifier_str_mv |
Proceedings Of The 6th International Conference On Operations Research And Enterprise Systems (icores). Setubal: Scitepress, p. 203-210, 2017. 10.5220/0006106402030210 WOS:000413254200019 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings Of The 6th International Conference On Operations Research And Enterprise Systems (icores) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
203-210 |
dc.publisher.none.fl_str_mv |
Scitepress |
publisher.none.fl_str_mv |
Scitepress |
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 |
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1808128813927759872 |