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Introducing the run support strategy for the bison algorithm

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dc.title Introducing the run support strategy for the bison algorithm en
dc.contributor.author Kazíková, Anežka
dc.contributor.author Pluháček, Michal
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Šenkeřík, Roman
dc.relation.ispartof Lecture Notes in Electrical Engineering
dc.identifier.issn 1876-1100 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 9783030149062
dc.date.issued 2020
utb.relation.volume 554
dc.citation.spage 272
dc.citation.epage 282
dc.event.title 5th International Conference on Advanced Engineering Theory and Applications, AETA 2018
dc.event.location Ostrava
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2018-09-11
dc.event.edate 2018-09-13
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-030-14907-9_27
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-030-14907-9_27
dc.subject Bison Algorithm en
dc.subject Exploration optimization en
dc.subject Run Support Strategy en
dc.description.abstract Many state-of-the-art optimization algorithms stand against the threat of premature convergence. While some metaheuristics try to avoid it by increasing the diversity in various ways, the Bison Algorithm faces this problem by guaranteeing stable exploitation – exploration ratio throughout the whole optimization process. Still, it is important to ensure, that the newly discovered solutions can affect the overall optimization process. In this paper, we propose a new Run Support Strategy for the Bison Algorithm, that should enhance the utilization of newly discovered solutions, and should be suitable for both continuous and discrete optimization. © Springer Nature Switzerland AG 2020. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008759
utb.identifier.obdid 43881255
utb.identifier.scopus 2-s2.0-85066308172
utb.source d-scopus
dc.date.accessioned 2019-08-13T10:17:18Z
dc.date.available 2019-08-13T10:17:18Z
utb.ou CEBIA-Tech
utb.contributor.internalauthor Kazíková, Anežka
utb.contributor.internalauthor Pluháček, Michal
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Šenkeřík, Roman
utb.fulltext.affiliation Anezka Kazikova, Michal Pluhacek, Tomas Kadavy, Roman Senkerik Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, 760 01 Zlin, Czech Republic {kazikova,pluhacek,kadavy,senkerik}@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 Faculty of Applied Informatics, Tomas Bata University in Zlin, T. G. Masaryka 5555, Zlin, 760 01, Czech Republic
utb.fulltext.projects LO1303
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 IC1406
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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