%0 Journal Article
%@holdercode {isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S}
%@nexthigherunit 8JMKD3MGPCW/3ESGTTP
%@archivingpolicy denypublisher denyfinaldraft24
%@dissemination WEBSCI; PORTALCAPES; COMPENDEX.
%@resumeid
%@resumeid 8JMKD3MGP5W/3C9JHMQ
%X The 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.
%@mirrorrepository sid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
%T Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem
%@secondarytype PRE PI
%K Clustering problems, Clustering search algorithm, Genetic Algorithm, Metaheuristics.
%@usergroup administrator
%@usergroup marciana
%@group
%@group LAC-CTE-INPE-MCT-BR
%3 Antonio Augusto Chaves.pdf
%@secondarykey INPE--PRE/
%@issn 0957-4174
%2 sid.inpe.br/mtc-m19/2011/02.15.17.17.02
%@affiliation Sao Paulo State University Julio de Mesquita Filho, Department of Mathematics
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%B Expert Systems with Applications
%P 5013–5018
%4 sid.inpe.br/mtc-m19/2011/02.15.17.17
%@documentstage not transferred
%D 2010
%V 38
%@doi 10.1016/j.eswa.2010.09.149
%A Chaves, Antonio Augusto,
%A Lorena, Luiz Antonio Nogueira,
%@area COMP