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

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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
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