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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 | 978-3-319-59059-2 | |
| 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 |
