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Boundary strategies for Self-Organizing Migrating Algorithm analyzed using CEC'17 Benchmark

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dc.title Boundary strategies for Self-Organizing Migrating Algorithm analyzed using CEC'17 Benchmark en
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Pluháček, Michal
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Viktorin, Adam
dc.relation.ispartof Communications in Computer and Information Science
dc.identifier.issn 1865-0929 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-03-037837-0
dc.date.issued 2020
utb.relation.volume 1092 CCIS
dc.citation.spage 58
dc.citation.epage 69
dc.event.title 7th International Conference on Swarm, Evolutionary, and Memetic Computing, SEMCCO 2019, and 5th International Conference on Fuzzy and Neural Computing, FANCCO 2019
dc.event.location Maribor
utb.event.state-en Slovenia
utb.event.state-cs Slovinsko
dc.event.sdate 2019-07-10
dc.event.edate 2019-07-12
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer
dc.identifier.doi 10.1007/978-3-030-37838-7_6
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-030-37838-7_6
dc.subject boundary en
dc.subject CEC17 en
dc.subject Friedman rank test en
dc.subject self-organizing migrating algorithm en
dc.subject SOMA en
dc.description.abstract This paper is focused on the influence of boundary strategies for the popular swarm-intelligence based optimization algorithm: Self-organizing Migrating Algorithm (SOMA). A similar extensive study was already performed for the most famous representative of swarm-based algorithm, which is Particle Swarm Optimization (PSO), and showed the importance of related research for other swarm-based techniques, like SOMA. The current CEC'17 benchmark suite is used for the performance comparison of the case studies, and the results are compared and tested for statistical significance using the Friedman Rank test. © Springer Nature Switzerland AG 2020. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009558
utb.identifier.obdid 43880945
utb.identifier.scopus 2-s2.0-85078397743
utb.source d-scopus
dc.date.accessioned 2020-02-11T10:07:40Z
dc.date.available 2020-02-11T10:07:40Z
utb.ou CEBIA-Tech
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Viktorin, Adam
utb.fulltext.affiliation Tomas Kadavy, Michal Pluhacek, Roman Senkerik, Adam Viktorin Tomas Bata University in Zlin, T.G. Masaryka 5555, 760 01 Zlin, Czech Republic {kadavy,pluhacek,senkerik,aviktorin}@utb.cz
utb.fulltext.dates -
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/2019/002. 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, T.G. Masaryka 5555, Zlin, 760 01, 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/2019/002
utb.fulltext.projects CA15140
utb.fulltext.projects ImAppNIO
utb.fulltext.projects IC1406
utb.fulltext.projects cHiPSet
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