Particle swarm optimization with single particle repulsivity for multi-modal optimization

Pluháček, Michal; Šenkeřík, Roman; Viktorin, Adam; Kadavý, Tomáš

dc.title	Particle swarm optimization with single particle repulsivity for multi-modal optimization	en
dc.contributor.author	Pluháček, Michal
dc.contributor.author	Šenkeřík, Roman
dc.contributor.author	Viktorin, Adam
dc.contributor.author	Kadavý, Tomáš
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	978-3-319-91252-3
dc.date.issued	2018
utb.relation.volume	10841 LNAI
dc.citation.spage	486
dc.citation.epage	494
dc.event.title	17th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2018
dc.event.location	Zakopane
utb.event.state-en	Poland
utb.event.state-cs	Polsko
dc.event.sdate	2018-06-03
dc.event.edate	2018-06-07
dc.type	conferenceObject
dc.language.iso	en
dc.publisher	Springer Verlag
dc.identifier.doi	10.1007/978-3-319-91253-0_45
dc.relation.uri	https://link.springer.com/chapter/10.1007/978-3-319-91253-0_45
dc.subject	Particle Swarm Optimization	en
dc.subject	PSO	en
dc.subject	Convergence	en
dc.subject	Repulsivity	en
dc.description.abstract	This work presents a simple but effective modification of the velocity updating formula in the Particle Swarm Optimization algorithm to improve the performance of the algorithm on multi-modal problems. The well-known issue of premature swarm convergence is addressed by a repulsive mechanism implemented on a single-particle level where each particle in the population is partially repulsed from a different particle. This mechanism manages to prolong the exploration phase and helps to avoid many local optima. The method is tested on well-known and typically used benchmark functions, and the results are further tested for statistical significance. © Springer International Publishing AG, part of Springer Nature 2018.	en
utb.faculty	Faculty of Applied Informatics
dc.identifier.uri	http://hdl.handle.net/10563/1008000
utb.identifier.obdid	43878971
utb.identifier.scopus	2-s2.0-85048045984
utb.identifier.wok	000552718500045
utb.source	d-scopus
dc.date.accessioned	2018-07-27T08:47:39Z
dc.date.available	2018-07-27T08:47:39Z
dc.description.sponsorship	IC1406, COST, European Cooperation in Science and Technology; CA15140, COST, European Cooperation in Science and Technology; IGA/CebiaTech/2018/003; MSMT-7778/2014, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; LO1303, MŠMT, Ministerstvo Školství, Mládeže a Tělovýchovy; COST, European Cooperation in Science and Technology; CZ.1.05/2.1.00/03.0089, FEDER, European Regional Development Fund
dc.description.sponsorship	Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme [LO1303 (MSMT-7778/2014)]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science Technology) [CA15140, IC1406]
utb.contributor.internalauthor	Pluháček, Michal
utb.contributor.internalauthor	Šenkeřík, Roman
utb.contributor.internalauthor	Viktorin, Adam
utb.contributor.internalauthor	Kadavý, Tomáš
utb.fulltext.affiliation	Michal Pluhacek ( ✉ ) http://orcid.org/0000-0002-3692-2838 , Roman Senkerik http://orcid.org/0000-0002-5839-4263 , Adam Viktorin http://orcid.org/0000-0003-0861-0340 , and Tomas Kadavy http://orcid.org/0000-0002-3341-4336 Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic {pluhacek,senkerik,aviktorin,kadavy}@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. Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, pp. 69–73 (1998) 3. Kennedy, J.: The particle swarm: social adaptation of knowledge. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997) 4. Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R.: A novel particle swarm optimization algorithm with adaptive inertia weight. Appl. Soft Comput. 11(4), 3658–3670 (2011). ISSN 1568-4946 5. Eberhart, R.C., Shi, Y.: Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, San Diego, USA, pp. 84–88 (2000) 6. Van Den Bergh, F., Engelbrecht, A.P.: A study of particle swarm optimization particle trajectories. Inf. Sci. 176(8), 937–971 (2006) 7. Riget, J., Vesterstrøm, J.S.: A diversity-guided particle swarm optimizer-the ARPSO. Technical Report, vol. 2, p. 2002. Department of Computer Science, University of Aarhus, Aarhus, Denmark (2002) 8. Han, F., Liu, Q.: An improved hybrid PSO based on ARPSO and the Quasi-Newton method. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 460–467. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20466-6 48 9. Engelbrecht, A.P.: Particle swarm optimization: iteration strategies revisited. In: 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, Ipojuca, pp. 119–123 (2013)
utb.fulltext.sponsorship	-
utb.wos.affiliation	[Pluhacek, Michal; Senkerik, Roman; Viktorin, Adam; Kadavy, Tomas] Tomas Bata Univ Zlin, Fac Appl Informat, TG Masaryka 5555, Zlin 76001, Czech Republic
utb.scopus.affiliation	Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, Czech Republic
utb.fulltext.projects	-