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Particle swarm optimization algorithm driven by multichaotic number generator

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dc.title Particle swarm optimization algorithm driven by multichaotic number generator en
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Soft Computing
dc.identifier.issn 1433-7479 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.issn 1432-7643 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2014
utb.relation.volume 18
utb.relation.issue 4
dc.citation.spage 631
dc.citation.epage 639
dc.type article
dc.language.iso en
dc.publisher Springer-Verlag
dc.identifier.doi 10.1007/s00500-014-1222-z
dc.relation.uri https://link.springer.com/article/10.1007/s00500-014-1222-z
dc.subject Chaos en
dc.subject Evolutionary algorithms en
dc.subject Optimization en
dc.subject Particle swarm en
dc.subject Swarm intelligence en
dc.description.abstract In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one run of the PSO algorithm and dynamically switched over when a certain criterion is met. By using this unique technique, it is possible to improve the performance of PSO algorithm as it is demonstrated on different benchmark functions. © 2014 Springer-Verlag Berlin Heidelberg. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1003719
utb.identifier.obdid 43871682
utb.identifier.scopus 2-s2.0-84897581363
utb.identifier.wok 000333030800003
utb.source j-scopus
dc.date.accessioned 2014-05-07T13:49:24Z
dc.date.available 2014-05-07T13:49:24Z
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
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