Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30865
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorYang, Shengxiang-
dc.date.accessioned2023-11-27T12:47:18Z-
dc.date.available2023-11-27T12:47:18Z-
dc.date.issued2013-01-01-
dc.identifier.citation16th European Conference on Applications of Evolutionary Computation, EvoApplications 2013, 3 - 5 April 2013en_US
dc.identifier.isbn9783642371912-
dc.identifier.issn03029743-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30865-
dc.description.abstractAnt colony optimization (ACO) algorithms have proved to be able to adapt to dynamic optimization problems (DOPs) when stagnation behaviour is avoided. Several approaches have been integrated with ACO to improve its performance for DOPs. The adaptation capabilities of ACO rely on the pheromone evaporation mechanism, where the rate is usually fixed. Pheromone evaporation may eliminate pheromone trails that represent bad solutions from previous environments. In this paper, an adaptive scheme is proposed to vary the evaporation rate in different periods of the optimization process. The experimental results show that ACO with an adaptive pheromone evaporation rate achieves promising results, when compared with an ACO with a fixed pheromone evaporation rate, for different DOPs. © Springer-Verlag Berlin Heidelberg 2013.en_US
dc.language.isoenen_US
dc.rights© Springer-Verlag Berlin Heidelbergen_US
dc.subjectDynamic routing algorithmsen_US
dc.subjectEvaporationen_US
dc.subjectAdaptive schemeen_US
dc.subjectAnt Colony Optimization algorithmsen_US
dc.subjectDynamic optimization problem (DOP)en_US
dc.subjectDynamic routingen_US
dc.subjectEvaporation rateen_US
dc.subjectPheromone trailsen_US
dc.titleAdapting the pheromone evaporation rate in dynamic routing problemsen_US
dc.typeConference Papersen_US
dc.collaborationDe Montfort Universityen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.relation.conferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.identifier.doi10.1007/978-3-642-37192-9_61en_US
dc.identifier.scopus2-s2.0-84875637343en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84875637343en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.volume7835 LNCSen_US
cut.common.academicyear2013-2014en_US
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_c94f-
item.fulltextNo Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairetypeconferenceObject-
crisitem.author.orcid0000-0002-5281-4175-
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