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

Pluháček, Michal; Šenkeřík, Roman; Zelinka, Ivan

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