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hal.structure.identifierSchool of Computer and Information Sciences
dc.contributor.authorPandiri, Venkatesh
hal.structure.identifierSchool of Computer and Information Sciences
dc.contributor.authorSingh, Alok
hal.structure.identifierLaboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
dc.contributor.authorRossi, André
HAL ID: 1694
dc.date.accessioned2021-01-08T11:30:51Z
dc.date.available2021-01-08T11:30:51Z
dc.date.issued2019
dc.identifier.issn0941-0643
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/21422
dc.language.isoenen
dc.subjectCovering salesman problemen
dc.subjectTraveling salesman problemen
dc.subjectHeuristicen
dc.subjectGenetic algorithmen
dc.subjectArtificial bee colony algorithmen
dc.subject.ddc005en
dc.titleTwo hybrid metaheuristic approaches for the covering salesman problemen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis paper addresses the covering salesman problem (CSP), which is an extension of the classical traveling salesman problem (TSP). Given a set of cities and a coverage radius associated with each one of them, the CSP seeks a Hamiltonian cycle over a subset of cities such that each city not in the subset is within the coverage radius of at least one city in the subset and that has minimum length among all Hamiltonian cycles over such subsets. To solve this problem, one has to deal with the aspects of subset selection and permutation. The CSP finds application in emergency and disaster management and rural healthcare. This paper presents two hybrid metaheuristic approaches for the CSP. The first approach is based on the artificial bee colony algorithm, whereas the latter approach is based on the genetic algorithm. Both the approaches make use of several new first improvement or best improvement based local search strategies defined over various neighborhood structures. Computational results on a wide range of benchmark instances demonstrate the effectiveness of the proposed approaches. We are able to improve the best known solution values on majority of the large instances.en
dc.relation.isversionofjnlnameNeural Computing and Applications
dc.relation.isversionofjnlvol32en
dc.relation.isversionofjnlissue19en
dc.relation.isversionofjnldate2020-10
dc.relation.isversionofjnlpages15643-15663en
dc.relation.isversionofdoi10.1007/s00521-020-04898-4en
dc.relation.isversionofjnlpublisherSpringeren
dc.subject.ddclabelProgrammation, logiciels, organisation des donnéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
dc.description.ssrncandidatenonen
dc.description.halcandidateouien
dc.description.readershiprechercheen
dc.description.audienceInternationalen
dc.relation.Isversionofjnlpeerreviewedouien
dc.relation.Isversionofjnlpeerreviewedouien
dc.date.updated2021-01-08T10:54:50Z
hal.identifierhal-03103742*
hal.version1*
hal.update.actionupdateFiles*
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