Adaptive biased random-key genetic algorithm with local search for the capacitated centered clustering problem

dc.contributor.authorChaves, Antonio Augusto [UNIFESP]
dc.contributor.authorLatteshttp://lattes.cnpq.br/4973949421738244
dc.date.accessioned2024-05-16T11:35:42Z
dc.date.available2024-05-16T11:35:42Z
dc.date.issued2018
dc.description.abstractThis paper proposes an adaptive Biased Random-key Genetic Algorithm (A-BRKGA), a new method with on-line parameter control for combinatorial optimization problems. A-BRKGA has only one problem-dependent component, the decoder and all other parts can be reused. To control diversification and intensification, a novel adaptive strategy for parameter tuning is introduced. This strategy is based on deterministic rules and self-adaptive schemes. For exploitation of specific regions of the solution space we propose a local search in promising communities. The proposed method is evaluated on the Capacitated Centered Clustering Problem (CCCP), which is an NP-hard problem where a set of n points, each having a given demand, is partitioned into m clusters each with a given capacity. The objective is to minimize the sum of the Euclidean distances between the points and their geometric cluster centroids. Computational results show that the A-BRKGA with local search is competitive with other methods of literature.
dc.description.sponsorshipID2018/15417-8
dc.identifier.doihttp://dx. doi.org/10.1016/j.cie.2018.07.031
dc.identifier.urihttps://hdl.handle.net/11600/71108
dc.languageeng
dc.publisherElsevier
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subjectmeta-heurísticas
dc.subjectotimização
dc.subjectlocalização
dc.titleAdaptive biased random-key genetic algorithm with local search for the capacitated centered clustering problem
dc.typeinfo:eu-repo/semantics/report
unifesp.campusInstituto de Ciência e Tecnologia (ICT)
unifesp.departamentoCiência e Tecnologia
unifesp.graduacaoNão se aplica
unifesp.graduateProgramCiência da Computação
unifesp.knowledgeAreaCiência da computação
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