Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30858
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorYang, Shengxiang-
dc.contributor.authorYao, Xin-
dc.date.accessioned2023-11-27T10:45:31Z-
dc.date.available2023-11-27T10:45:31Z-
dc.date.issued2014-01-12-
dc.identifier.citation2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, CIDUE 2014, Orlando, Florida, 9 - 12 December 2014en_US
dc.identifier.isbn9781479945160-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30858-
dc.description.abstractA multi-colony ant colony optimization (ACO) algorithm consists of several colonies of ants. Each colony uses a separate pheromone table in an attempt to maximize the search area explored. Over the years, multi-colony ACO algorithms have been successfully applied on different optimization problems with stationary environments. In this paper, we investigate their performance in dynamic environments. Two types of algorithms are proposed: homogeneous and heterogeneous approaches, where colonies share the same properties and colonies have their own (different) properties, respectively. Experimental results on the dynamic travelling salesman problem show that multi-colony ACO algorithms have promising performance in dynamic environments when compared with single colony ACO algorithms.en_US
dc.language.isoenen_US
dc.rights© IEEEen_US
dc.subjectAnt colony optimizationen_US
dc.subjectArtificial intelligenceen_US
dc.subjectACO algorithmsen_US
dc.subjectAnt Colony Optimization algorithmsen_US
dc.subjectDynamic environmentsen_US
dc.subjectMulti-colony ant algorithmsen_US
dc.subjectOptimization problemsen_US
dc.subjectSearch areaen_US
dc.subjectStationary environmentsen_US
dc.subjectTravelling salesman problemen_US
dc.subjectTraveling salesman problemen_US
dc.titleMulti-colony ant algorithms for the dynamic travelling salesman problemen_US
dc.typeConference Papersen_US
dc.collaborationDe Montfort Universityen_US
dc.collaborationUniversity of Birminghamen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.relation.conferenceIEEE SSCI 2014: 2014 IEEE Symposium Series on Computational Intelligence - CIDUE 2014: 2014 IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Proceedingsen_US
dc.identifier.doi10.1109/CIDUE.2014.7007861en_US
dc.identifier.scopus2-s2.0-84922928085en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84922928085en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
cut.common.academicyear2014-2015en_US
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.openairetypeconferenceObject-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.languageiso639-1en-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0002-5281-4175-
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