dc.contributorInstituto Nacional de Pesquisas Espaciais (INPE)
dc.contributorUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-20T13:28:14Z
dc.date.available2014-05-20T13:28:14Z
dc.date.created2014-05-20T13:28:14Z
dc.date.issued2011-05-01
dc.identifierExpert Systems With Applications. Oxford: Pergamon-Elsevier B.V. Ltd, v. 38, n. 5, p. 5013-5018, 2011.
dc.identifier0957-4174
dc.identifierhttp://hdl.handle.net/11449/9379
dc.identifier10.1016/j.eswa.2010.09.149
dc.identifierWOS:000287419900040
dc.description.abstractThe Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. (C) 2010 Elsevier Ltd. All rights reserved.
dc.languageeng
dc.publisherPergamon-Elsevier B.V. Ltd
dc.relationExpert Systems with Applications
dc.relation3.768
dc.relation1,271
dc.rightsAcesso restrito
dc.sourceWeb of Science
dc.subjectClustering problems
dc.subjectClustering search algorithm
dc.subjectGenetic Algorithm
dc.subjectMetaheuristics
dc.titleHybrid evolutionary algorithm for the Capacitated Centered Clustering Problem
dc.typeArtículos de revistas


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