Improving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithms

dc.contributor.authorMoraes Barbosa, Eduardo Batista de
dc.contributor.authorFranca Senne, Edson Luiz [UNESP]
dc.contributor.institutionBrazilian Inst Space Res INPE
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2018-11-26T15:44:23Z
dc.date.available2018-11-26T15:44:23Z
dc.date.issued2017-01-01
dc.description.abstractUsually, 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.en
dc.description.affiliationBrazilian Inst Space Res INPE, Cachoeira Paulista, SP, Brazil
dc.description.affiliationUniv Estadual Paulista, UNESP, Guaratingueta, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, UNESP, Guaratingueta, SP, Brazil
dc.format.extent7
dc.identifierhttp://dx.doi.org/10.1155/2017/8042436
dc.identifier.citationJournal Of Optimization. London: Hindawi Ltd, 7 p., 2017.
dc.identifier.doi10.1155/2017/8042436
dc.identifier.fileWOS000403725100001.pdf
dc.identifier.issn2356-752X
dc.identifier.urihttp://hdl.handle.net/11449/159588
dc.identifier.wosWOS:000403725100001
dc.language.isoeng
dc.publisherHindawi Ltd
dc.relation.ispartofJournal Of Optimization
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.titleImproving the Fine-Tuning of Metaheuristics: An Approach Combining Design of Experiments and Racing Algorithmsen
dc.typeArtigo
dcterms.rightsHolderHindawi Ltd
unesp.author.orcid0000-0002-9492-3302[1]

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