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Extended experimental study on PSO with partial population restart based on complex network analysis

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dc.title Extended experimental study on PSO with partial population restart based on complex network analysis en
dc.contributor.author Pluháček, Michal
dc.contributor.author Viktorin, Adam
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Logic Journal of the IGPL
dc.identifier.issn 1367-0751 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2020
utb.relation.volume 28
utb.relation.issue 2
dc.citation.spage 211
dc.citation.epage 225
dc.type article
dc.language.iso en
dc.publisher Oxford University Press
dc.identifier.doi 10.1093/jigpal/jzy046
dc.relation.uri https://academic.oup.com/jigpal/article-abstract/28/2/211/5107039?redirectedFrom=fulltext
dc.subject swarm intelligence en
dc.subject particle swarm optimization en
dc.subject complex network en
dc.subject hybrid method en
dc.description.abstract This extended study presents a hybridization of particle swarm optimization (PSO) with complex network construction and analysis. A partial population restart is performed in certain moments of the run of the algorithm based on the information obtained from a complex network analysis. The complex network structure represents the communication in the population. We present experimental results of the method alongside with statistical evaluation and discuss future possibilities of this approach. The main goal of the work is not to propose a new highly competitive PSO variant but to present the possibility of using the unconventional tool as an alternative to conventional diversity measures. The main benefit of the network analysis is that it has same-time requirements regardless of the dimension of the problem. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009872
utb.identifier.obdid 43881324
utb.identifier.scopus 2-s2.0-85101349479
utb.identifier.wok 000559006600006
utb.source J-wok
dc.date.accessioned 2020-09-01T10:09:21Z
dc.date.available 2020-09-01T10:09:21Z
dc.description.sponsorship Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme Project [LO1303, MSMT-7778/2014]; Internal Grant Agency of Tomas Bata University [IGA/CebiaTech/2018/003]; COST (European Cooperation in Science & Technology), Improving Applicability of NatureInspired Optimisation by Joining Theory and Practice (ImAppNIO) [CA15140]; SGS, VSB-Technical University of Ostrava [SGS 2018/177]; European Regional Development Fund under the Project CEBIA-Tech [CZ.1.05/2.1.00/03.0089]; COST (European Cooperation in Science & Technology), HighPerformance Modelling and Simulation for Big Data Applications (cHiPSet) [IC1406]
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Kadavý, Tomáš
utb.fulltext.affiliation MICHAL PLUHACEK *, ADAM VIKTORIN **, ROMAN SENKERIK†, TOMAS KADAVY ††, IVAN ZELINKA § Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01Zlin, Czech Republic Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam and Department of Computer Science Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Czech Republic ∗ E-mail: pluhacek@utb.cz ∗∗ E-mail: aviktorin@utb.cz † E-mail: senkerik@utb.cz †† E-mail: kadavy@utb.cz § E-mail: ivan.zelinka@tdt.edu.vn; ivan.zelinka@vsb.cz
utb.fulltext.dates Received 25 September 2017
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 grant of SGS No. SGS 2018/177, VSB-Technical University of Ostrava. resources of A.I.Lab at the Faculty of Applied Informatics, Tomas Bata University in Zlin (ailab.fai.utb.cz).
utb.wos.affiliation [Pluhacek, Michal; Viktorin, Adam; Senkerik, Roman; Kadavy, Tomas] Tomas Bata Univ Zlin, Fac Appl Informat, Nam TG Masaryka 5555, Zlin 76001, Czech Republic; [Zelinka, Ivan] Ton Duc Thang Univ, Fac Elect & Elect Engn, Modeling Evolutionary Algorithms Simulat & Artifi, Ho Chi Minh City, Vietnam; [Zelinka, Ivan] VSB Tech Univ Ostrava, Fac Elect Engn & Comp Sci, Dept Comp Sci, Ostrava, Czech Republic
utb.scopus.affiliation Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, Zlin, 760 01, Czech Republic; Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 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 CA15140
utb.fulltext.projects ImAppNIO
utb.fulltext.projects IC1406
utb.fulltext.projects cHiPSet
utb.fulltext.projects SGS 2018/177
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
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