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Multiple choice strategy - A novel approach for particle swarm optimization - Preliminary study

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dc.title Multiple choice strategy - A novel approach for particle swarm optimization - Preliminary study en
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Artificial Intelligence and Soft Computing 2014, Part II
dc.identifier.issn 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-642-38609-1
dc.date.issued 2013
utb.relation.volume 7895 LNAI
utb.relation.issue PART 2
dc.citation.spage 36
dc.citation.epage 45
dc.event.title 12th International Conference on Artificial Intelligence and Soft Computing, ICAISE 2013
dc.event.location Zakopane
utb.event.state-en Poland
utb.event.state-cs Polsko
dc.event.sdate 2013-06-09
dc.event.edate 2013-06-13
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer-Verlag Berlin en
dc.identifier.doi 10.1007/978-3-642-38610-7_4
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-642-38610-7_4
dc.description.abstract In this paper, a new promising strategy for PSO algorithm is proposed and described. This strategy presents alternative way of assigning new velocity to each particle in population. This new multiple choice particle swarm optimization (MC-PSO) algorithm is tested on four different test functions and the promising results of this alternative strategy are compared with the classic method. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1003578
utb.identifier.obdid 43870271
utb.identifier.scopus 2-s2.0-84884378297
utb.identifier.wok 000326950400004
utb.source d-wok
dc.date.accessioned 2013-12-18T16:07:43Z
dc.date.available 2013-12-18T16:07:43Z
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Zelinka, Ivan
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