Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/30863
DC FieldValueLanguage
dc.contributor.authorMavrovouniotis, Michalis-
dc.contributor.authorYang, Shengxiang-
dc.date.accessioned2023-11-27T11:43:44Z-
dc.date.available2023-11-27T11:43:44Z-
dc.date.issued2013-01-01-
dc.identifier.citationStudies in Computational Intelligence, 2013, vol. 490, pp. 317 - 341en_US
dc.identifier.isbn9783642384158-
dc.identifier.issn1860949X-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/30863-
dc.description.abstractAnt colony optimization (ACO) algorithms have proved to be powerful methods to address dynamic optimization problems (DOPs). However, once the population converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to the new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution is to maintain the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants. In this chapter, we investigate ACO algorithms with different immigrants schemes for two types of dynamic travelling salesman problems (DTSPs) with traffic factor, i.e., under random and cyclic dynamic changes. The experimental results based on different DTSP test cases show that the investigated algorithms outperform other peer ACO algorithms and that different immigrants schemes are beneficial on different environmental cases. © 2013 Springer-Verlag Berlin Heidelberg.en_US
dc.language.isoenen_US
dc.relation.ispartofStudies in Computational Intelligenceen_US
dc.rights© Springer-Verlag Berlin Heidelbergen_US
dc.subjectAnt colonyen_US
dc.subjectimmigrants schemesen_US
dc.subjectsalesman problemen_US
dc.titleAnt colony optimization algorithms with immigrants schemes for the dynamic travelling salesman problemen_US
dc.typeArticleen_US
dc.collaborationDe Montfort Universityen_US
dc.subject.categoryComputer and Information Sciencesen_US
dc.journalsSubscriptionen_US
dc.countryUnited Kingdomen_US
dc.subject.fieldNatural Sciencesen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1007/978-3-642-38416-5_13en_US
dc.identifier.scopus2-s2.0-84884258938en
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/84884258938en
dc.contributor.orcid#NODATA#en
dc.contributor.orcid#NODATA#en
dc.relation.volume490en_US
cut.common.academicyear2013-2014en_US
dc.identifier.spage317en_US
dc.identifier.epage341en_US
item.grantfulltextnone-
item.languageiso639-1en-
item.cerifentitytypePublications-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.openairetypearticle-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0002-5281-4175-
Appears in Collections:Άρθρα/Articles
CORE Recommender
Show simple item record

SCOPUSTM   
Citations 20

7
checked on Mar 14, 2024

Page view(s)

78
Last Week
0
Last month
3
checked on Nov 7, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.