Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30850
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorMüller, Felipe Martins-
dc.contributor.authorYang, Shengxiang-
dc.date.accessioned2023-11-23T12:26:17Z-
dc.date.available2023-11-23T12:26:17Z-
dc.date.issued2015-07-11-
dc.identifier.citation16th Genetic and Evolutionary Computation Conference, GECCO 2015, Madrid, Spain, 11 - 15 July 2015en_US
dc.identifier.isbn9781450334723-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30850-
dc.description.abstractAnt colony optimization (ACO) algorithms have proved to be able to adapt for solving dynamic optimization problems (DOPs). The integration of local search algorithms has also proved to significantly improve the output of ACO algorithms. However, almost all previous works consider stationary environments. In this paper, the MAX-MIN Ant System, one of the best ACO variations, is integrated with the unstringing and stringing (US) local search operator for the dynamic travelling salesman problem (DTSP). The best solution constructed by ACO is passed to the US operator for local search improvements. The proposed memetic algorithm aims to combine the adaptation capabilities of ACO for DOPs and the superior performance of the US operator on the static travelling salesman problem in order to tackle the DTSP. The experiments show that the MAX-MIN Ant System is able to provide good initial solutions to US and the proposed algorithm outperforms other peer ACO-based memetic algorithms on different DTSPs.en_US
dc.language.isoenen_US
dc.rights© ACMen_US
dc.subjectAnt colony optimizationen_US
dc.subjectDynamic travelling salesman problemen_US
dc.subjectLocal searchen_US
dc.subjectMemetic computingen_US
dc.titleAn ant colony optimization based memetic algorithm for the dynamic travelling salesman problemen_US
dc.typeConference Papersen_US
dc.collaborationDe Montfort Universityen_US
dc.collaborationFederal University of Santa Mariaen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryUnited Kingdomen_US
dc.countryBrazilen_US
dc.subject.fieldNatural Sciencesen_US
dc.relation.conferenceGECCO 2015 - Proceedings of the 2015 Genetic and Evolutionary Computation Conferenceen_US
dc.identifier.doi10.1145/2739480.2754651en_US
dc.identifier.scopus2-s2.0-84963656339en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84963656339en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyearemptyen_US
item.languageiso639-1en-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextNo Fulltext-
item.openairetypeconferenceObject-
crisitem.author.orcid0000-0002-5281-4175-
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