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Comparing strategies for search space boundaries violation in PSO

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dc.title Comparing strategies for search space boundaries violation in PSO en
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Pluháček, Michal
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.identifier.issn 0302-9743 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783319590592
dc.date.issued 2017
utb.relation.volume 10246 LNAI
dc.citation.spage 655
dc.citation.epage 664
dc.event.title 16th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2017
dc.event.location Zakopane
utb.event.state-en Poland
utb.event.state-cs Polsko
dc.event.sdate 2017-06-11
dc.event.edate 2017-06-15
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-59060-8_59
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-59060-8_59
dc.subject ARPSO en
dc.subject Boundaries en
dc.subject CEC en
dc.subject Particle Swarm Optimization en
dc.subject PSO en
dc.subject Search space en
dc.description.abstract In this paper, we choose to compare four methods for controlling particle position when it violates the search space boundaries and the impact on the performance of Particle Swarm Optimization algorithm (PSO). The methods are: hard borders, soft borders, random position and spherical universe. The goal is to compare the performance of these methods for the classical version of PSO and popular modification - the Attractive and Repulsive Particle Swarm Optimization (ARPSO). The experiments were carried out according to CEC benchmark rules and statistically evaluated. © Springer International Publishing AG 2017. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007183
utb.identifier.obdid 43877170
utb.identifier.scopus 2-s2.0-85020874501
utb.identifier.wok 000426206100059
utb.source d-scopus
dc.date.accessioned 2017-09-03T21:39:57Z
dc.date.available 2017-09-03T21:39:57Z
dc.description.sponsorship Grant Agency of the Czech Republic - GACR [P103/15/06700S]; Ministry of Education, Youth and Sports of the Czech Republic within National Sustainability Programme Project [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2017/004]
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Tomas Kadavy ( B ) , Michal Pluhacek, Adam Viktorin, and Roman Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {kadavy,pluhacek,aviktorin,senkerik}@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references 1. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995) 2. Nepomuceno, F.V., Engelbrecht, A.P.: A self-adaptive heterogeneous PSO for real-parameter optimization. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 361–368. IEEE (2013) 3. Zhan, Z.-H., et al.: Orthogonal learning particle swarm optimization. IEEE Trans. Evol. Comput. 15(6), 832–847 (2011) 4. Riget, J., Vesterstrom, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Department of Computer Science, University of Aarhus, Aarhus, Denmark, Technical report 2002–02 (2002) 5. Chen, Q., et al.: Problem definitions and evaluation criteria for CEC 2015 special session on bound constrained single-objective computationally expensive numerical optimization 6. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997) 7. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the 2000 Congress on Evolutionary Computation, CEC 2000, pp. 84–88. IEEE (2000) 8. Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. Am. Stat. Assoc. 32, 675–701 (1937) 9. Demsar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
utb.fulltext.sponsorship This work was supported by Grant Agency of the Czech Republic – GACR P103/15/06700S, further by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014). Also by the European Regional Development Fund under the Project CEBIA-Tech no. CZ.1.05/2.1.00/03.0089 and by Internal Grant Agency of Tomas Bata University under the Projects no. IGA/CebiaTech/2017/004.
utb.wos.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, T.G. Masaryka 5555, Zlin, Czech Republic
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