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dc.contributor.authorJakšić Kruger, Tatjanaen
dc.contributor.authorDavidović, Tatjanaen
dc.contributor.authorTeodorović, Dušanen
dc.contributor.authorŠelmić, Milicaen
dc.date.accessioned2020-04-03T08:16:02Z-
dc.date.available2020-04-03T08:16:02Z-
dc.date.issued2016-01-01en
dc.identifier.issn1758-0366en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/255-
dc.description.abstractThe bee colony optimization (BCO) algorithm is a nature-inspired meta-heuristic method for dealing with hard, real-life combinatorial and continuous optimisation problems. It is based on the foraging habits of honeybees and was proposed by Lučić and Teodorović in 2001. BCO is a simple, but effective meta-heuristic method that has already been successfully applied to various combinatorial optimisation problems in transport, location analysis, scheduling and some other fields. This paper provides theoretical verification of the BCO algorithm by proving some convergence properties. As a result, the gap between successful practice and missing theory is reduced.en
dc.publisherInderscience Publishers-
dc.relation.ispartofInternational Journal of Bio-Inspired Computationen
dc.subjectBio-inspired algorithms | Convergence properties | Foraging of honeybees | Global optimum | Meta-heuristic methods | Optimisation problems | Stochastic processes | Swarm intelligence | The bco algorithm | Theoretical analysisen
dc.titleThe bee colony optimization algorithm and its convergenceen
dc.typeArticleen
dc.identifier.doi10.1504/IJBIC.2016.079573en
dc.identifier.scopus2-s2.0-84992205902en
dc.relation.firstpage340en
dc.relation.lastpage354en
dc.relation.issue5en
dc.relation.volume8en
dc.description.rankM21a-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0001-6766-4811-
crisitem.author.orcid0000-0001-9561-5339-
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