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dc.contributor.authorCrainic, Teodor Gabrielen
dc.contributor.authorGendreau, Michelen
dc.contributor.authorHansen, Pierreen
dc.contributor.authorMladenović, Nenaden
dc.date.accessioned2020-05-02T16:42:15Z-
dc.date.available2020-05-02T16:42:15Z-
dc.date.issued2004-01-01en
dc.identifier.issn1381-1231en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2551-
dc.description.abstractWe propose a cooperative multi-search method for the Variable Neighborhood Search (VNS) meta-heuristic based on the central-memory mechanism that has been successfully applied to a number of difficult combinatorial problems. In this approach, several independent VNS meta-heuristics cooperate by asynchronously exchanging information about the best solutions identified so far, thus conserving the simplicity of the original, sequential VNS ideas. The p-median problem (PM) serves as test case. Extensive experimentations have been conducted on the classical TSPLIB benchmark problem instances with up to 11948 customers and 1000 medians, without any particular calibration of the parallel method. The results indicate that, compared to sequential VNS, the cooperative strategy yields significant gains in terms of computation time without a loss in solution quality.en
dc.publisherSpringer Link-
dc.relation.ispartofJournal of Heuristicsen
dc.subjectCooperative search | p-median problem | Parallel optimization | Variable Neighborhood Searchen
dc.titleCooperative parallel variable neighborhood search for the p-medianen
dc.typeArticleen
dc.identifier.doi10.1023/B:HEUR.0000026897.40171.1aen
dc.identifier.scopus2-s2.0-3543053628en
dc.relation.firstpage293en
dc.relation.lastpage314en
dc.relation.issue3en
dc.relation.volume10en
dc.description.rankM22-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0001-6655-0409-
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