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Ensemble of strategies and perturbation parameter based SOMA for optimal stabilization of chaotic oscillations

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dc.title Ensemble of strategies and perturbation parameter based SOMA for optimal stabilization of chaotic oscillations en
dc.contributor.author Šenkeřík, Roman
dc.contributor.author Kadavý, Tomáš
dc.contributor.author Viktorin, Adam
dc.contributor.author Pluháček, Michal
dc.relation.ispartof GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion
dc.identifier.isbn 978-1-4503-7127-8
dc.date.issued 2020
dc.citation.spage 1468
dc.citation.epage 1475
dc.event.title 2020 Genetic and Evolutionary Computation Conference, GECCO 2020
dc.event.location Cancún
utb.event.state-en Mexico
utb.event.state-cs Mexiko
dc.event.sdate 2020-07-08
dc.event.edate 2020-07-12
dc.type conferenceObject
dc.language.iso en
dc.publisher Association for Computing Machinery, Inc
dc.identifier.doi 10.1145/3377929.3398145
dc.relation.uri https://dl.acm.org/doi/10.1145/3377929.3398145
dc.subject chaos en
dc.subject differential evolution en
dc.subject nonliner control en
dc.subject optimization en
dc.subject SOMA en
dc.description.abstract In this paper, we are investigating an original ensemble based adaptive strategy for the Self Organizing Migrating Algorithm (SOMA), namely the "Ensemble of Strategies and Perturbation Parameter in SOMA" (ESP-SOMA). The algorithm to which the main attention is focused as well as other state of the art metaheuristic algorithm (SHADE) are utilized in the complex task of time and quality optimal stabilization of chaotic system oscillations. Since there is a growing demand for intelligent and fast problem solutions in engineering, this paper represents an insight into the applicability and effectivity of modern adaptive/state of the art metaheuristic optimization algorithms in the task of constrained and difficult optimization problem. Experiments encompass two different cases of desired behaviour of chaotic system, and two different designs of objective function in terms of complexity. The simple statistical comparison of the results given by four different metaheuristic algorithms is also reported here for the pairs of generic and enhanced versions. © 2020 ACM. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1009857
utb.identifier.obdid 43882305
utb.identifier.scopus 2-s2.0-85089740873
utb.source d-scopus
dc.date.accessioned 2020-09-01T10:09:20Z
dc.date.available 2020-09-01T10:09:20Z
utb.contributor.internalauthor Šenkeřík, Roman
utb.contributor.internalauthor Kadavý, Tomáš
utb.contributor.internalauthor Viktorin, Adam
utb.contributor.internalauthor Pluháček, Michal
utb.fulltext.affiliation Roman Šenkeřík, Tomas Kadavy, Adam Viktorin, Michal Pluháček Tomas Bata University in Zlin, Zlin, Czech Republic Tomas Bata University in Zlin, Zlin, Czech Republic Tomas Bata University in Zlin, Zlin, Czech Republic Tomas Bata University in Zlin, Zlin, Czech Republic
utb.fulltext.dates -
utb.scopus.affiliation Tomas Bata University in Zlin, Zlin, Czech Republic
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