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Particle swarm optimization with distance based repulsivity

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dc.title Particle swarm optimization with distance based repulsivity en
dc.contributor.author Pluháček, Michal
dc.contributor.author Zelinka, Ivan
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.contributor.author Kadavý, Tomáš
dc.relation.ispartof Mendel
dc.identifier.issn 1803-3814 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2018
utb.relation.volume 24
utb.relation.issue 2
dc.citation.spage 81
dc.citation.epage 86
dc.type article
dc.language.iso en
dc.publisher Brno University of Technology
dc.identifier.doi 10.13164/mendel.2018.2.081
dc.relation.uri https://mendel-journal.org/index.php/mendel/article/view/21
dc.subject Distance based en
dc.subject Particle swarm optimization en
dc.subject Repulsive en
dc.subject Swarm intelligence en
dc.description.abstract In this study, we propose a repulsive mechanism for the Particle Swarm Optimization algorithm that improves its performance on multi-modal problems. The repulsive mechanism is further extended with a distance-based modification. The results are presented and tested for statistical significance. We discuss the observations and propose further directions for the research. © 2018, Brno University of Technology. All rights reserved. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009086
utb.identifier.scopus 2-s2.0-85071969266
utb.source j-scopus
dc.date.accessioned 2019-09-19T07:56:15Z
dc.date.available 2019-09-19T07:56:15Z
dc.rights Attribution-NonCommercial-ShareAlike 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.access openAccess
utb.ou CEBIA-Tech
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.sponsorship This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project no. LO1303 (MSMT-7778/2014), further 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/2018/003. This work is also based upon support by COST (European Cooperation in Science & Technology) under Action CA15140, Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), and Action IC1406, High-Performance Modelling and Simulation for Big Data Applications (cHiPSet). The work was further supported by resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.scopus.affiliation Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, Zlin, 760 05, Czech Republic; Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, 17. listopadu 15, Ostrava-Poruba, 708 33, Czech Republic
utb.fulltext.projects LO1303
utb.fulltext.projects MSMT-7778/2014
utb.fulltext.projects CZ.1.05/2.1.00/03.0089
utb.fulltext.projects IGA/CebiaTech/2018/003
utb.fulltext.projects ImAppNIO
utb.fulltext.projects IC1406
utb.fulltext.projects cHiPSet
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