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Gathering algorithm: A new concept of PSO based metaheuristic with dimensional mutation

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dc.title Gathering algorithm: A new concept of PSO based metaheuristic with dimensional mutation en
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Zelinka, Ivan
dc.contributor.author Davendra, Donald David
dc.relation.ispartof IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - SIS 2014: 2014 IEEE Symposium on Swarm Intelligence, Proceedings
dc.identifier.isbn 9781479944590
dc.date.issued 2015
dc.citation.spage 42
dc.citation.epage 47
dc.event.title 2014 IEEE Symposium on Swarm Intelligence, SIS 2014
dc.event.location Orlando, FL
utb.event.state-en United States
utb.event.state-cs Spojené státy americké
dc.event.sdate 2014-12-09
dc.event.edate 2014-12-12
dc.type article
dc.language.iso en
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.identifier.doi 10.1109/SIS.2014.7011774
dc.relation.uri http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7011774
dc.subject Dimensional mutation en
dc.subject Particle swarm optimization en
dc.subject PSO en
dc.subject Snowball effect en
dc.description.abstract In this paper, a novel PSO based metaheuristic is proposed. This described approach is inspired by human gathering mechanisms. Each particle is given a possibility to follow a randomly selected particle from the swarm. When a promising search area is found by the particle, it remains stationary for a given number of iterations improving the chances of other particles following such a stationary particle into that search area. In this novel concept, the location of global best solution is not used as the attraction point for the particles. But the convergence into promising search areas is driven by the snowball effect of increasing number of stationary particles in the particular promising areas. Two different dimensional mutations are applied on stationary particles for the further improvement the performance of the algorithm. The key mechanism of the algorithm is described here in detail. The performance is tested on the CEĆ13 benchmark set with promising results. The results are compared with two current state-of-art PSO based optimization techniques. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1004171
utb.identifier.obdid 43872927
utb.identifier.scopus 2-s2.0-84923103722
utb.identifier.wok 000364912700008
utb.source j-wok
dc.date.accessioned 2015-04-13T14:07:00Z
dc.date.available 2015-04-13T14:07:00Z
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Zelinka, Ivan
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