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dc.contributor.authorDavidović, Tatjanaen
dc.contributor.authorJakšić Kruger, Tatjanaen
dc.date.accessioned2020-04-03T08:16:01Z-
dc.date.available2020-04-03T08:16:01Z-
dc.date.issued2018-01-01en
dc.identifier.isbn978-3-319-93799-1en
dc.identifier.issn1865-0929en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/246-
dc.description.abstractIntensive applications and success of metaheuristics in practice have initiated research on their theoretical analysis. Due to the unknown quality of reported solution(s) and the inherently stochastic nature of metaheuristics, the theoretical analysis of their asymptotic convergence towards a global optimum is mainly conducted by means of probability theory. In this paper, we show that principles developed for the theoretical analysis of Bee Colony Optimization metaheuristic hold for swarm intelligence based metaheuristics: they need to implement learning mechanisms in order to properly adapt the probability rule for modification of a candidate solution. We propose selection schemes that a swarm intelligence based metaheuristic needs to incorporate in order to assure the so-called model convergence.en
dc.publisherSpringer Link-
dc.relationGraph theory and mathematical programming with applications in chemistry and computer science-
dc.relationSerbian Ministry of Education, Science and Technological Development, Grant no. F159-
dc.relationDevelopment of new information and communication technologies, based on advanced mathematical methods, with applications in medicine, telecommunications, power systems, protection of national heritage and education-
dc.relation.ispartofCommunications in Computer and Information Scienceen
dc.subjectAsymptotic properties | Nature-inspired methods | Optimization problems | Solution quality | Stochastic processesen
dc.titleConvergence analysis of swarm intelligence metaheuristic methodsen
dc.typeConference Paperen
dc.relation.conference7th International Conference on Optimization Problems and Their Applications, OPTA 2018; Omsk; Russian Federation; 8 June 2018 through 14 June 2018-
dc.identifier.doi10.1007/978-3-319-93800-4_20en
dc.identifier.scopus2-s2.0-85049675809en
dc.relation.firstpage251en
dc.relation.lastpage266en
dc.relation.volume871en
item.openairetypeConference Paper-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
crisitem.author.orcid0000-0001-9561-5339-
crisitem.author.orcid0000-0001-6766-4811-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/174033e.php-
crisitem.project.projectURLhttp://www.mi.sanu.ac.rs/novi_sajt/research/projects/044006e.php-
crisitem.project.fundingProgramDirectorate for Computer & Information Science & Engineering-
crisitem.project.fundingProgramNATIONAL HEART, LUNG, AND BLOOD INSTITUTE-
crisitem.project.openAireinfo:eu-repo/grantAgreement/NSF/Directorate for Computer & Information Science & Engineering/1740333-
crisitem.project.openAireinfo:eu-repo/grantAgreement/NIH/NATIONAL HEART, LUNG, AND BLOOD INSTITUTE/5R01HL044006-04-
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