Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1155/2017/8042436 http://hdl.handle.net/11449/159588 |
Resumo: | Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present 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 close to a promising alternative. 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 the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach. |
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Repositório Institucional da UNESP |
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2946 |
spelling |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing AlgorithmsUsually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present 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 close to a promising alternative. 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 the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach.Brazilian Inst Space Res INPE, Cachoeira Paulista, SP, BrazilUniv Estadual Paulista, UNESP, Guaratingueta, SP, BrazilUniv Estadual Paulista, UNESP, Guaratingueta, SP, BrazilHindawi LtdBrazilian Inst Space Res INPEUniversidade Estadual Paulista (Unesp)Moraes Barbosa, Eduardo Batista deFranca Senne, Edson Luiz [UNESP]2018-11-26T15:44:23Z2018-11-26T15:44:23Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article7application/pdfhttp://dx.doi.org/10.1155/2017/8042436Journal Of Optimization. London: Hindawi Ltd, 7 p., 2017.2356-752Xhttp://hdl.handle.net/11449/15958810.1155/2017/8042436WOS:000403725100001WOS000403725100001.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengJournal Of Optimizationinfo:eu-repo/semantics/openAccess2024-01-15T06:18:28Zoai:repositorio.unesp.br:11449/159588Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T23:00:55.043451Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
title |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
spellingShingle |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms Moraes Barbosa, Eduardo Batista de |
title_short |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
title_full |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
title_fullStr |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
title_full_unstemmed |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
title_sort |
Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms |
author |
Moraes Barbosa, Eduardo Batista de |
author_facet |
Moraes Barbosa, Eduardo Batista de Franca Senne, Edson Luiz [UNESP] |
author_role |
author |
author2 |
Franca Senne, Edson Luiz [UNESP] |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Brazilian Inst Space Res INPE Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Moraes Barbosa, Eduardo Batista de Franca Senne, Edson Luiz [UNESP] |
description |
Usually, metaheuristic algorithms are adapted to a large set of problems by applying few modifications on parameters for each specific case. However, this flexibility demands a huge effort to correctly tune such parameters. Therefore, the tuning of metaheuristics arises as one of the most important challenges in the context of research of these algorithms. Thus, this paper aims to present 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 close to a promising alternative. 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 the classical traveling salesman problem. The results are compared considering the same metaheuristics tuned through a racing method. Broadly, the proposed approach proved to be effective in terms of the overall time of the tuning process. Our results reveal that metaheuristics tuned by means of HORA achieve, with much less computational effort, similar results compared to the case when they are tuned by the other fine-tuning approach. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-11-26T15:44:23Z 2018-11-26T15:44:23Z |
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://dx.doi.org/10.1155/2017/8042436 Journal Of Optimization. London: Hindawi Ltd, 7 p., 2017. 2356-752X http://hdl.handle.net/11449/159588 10.1155/2017/8042436 WOS:000403725100001 WOS000403725100001.pdf |
url |
http://dx.doi.org/10.1155/2017/8042436 http://hdl.handle.net/11449/159588 |
identifier_str_mv |
Journal Of Optimization. London: Hindawi Ltd, 7 p., 2017. 2356-752X 10.1155/2017/8042436 WOS:000403725100001 WOS000403725100001.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Journal Of Optimization |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
7 application/pdf |
dc.publisher.none.fl_str_mv |
Hindawi Ltd |
publisher.none.fl_str_mv |
Hindawi Ltd |
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_ |
1808129482224041984 |