Chaotic golden ratio guided local search for big data optimization

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dc.authoridKOCER, Havva Gul/0000-0003-4083-722X
dc.authoridTurkoglu, Bahaeddin/0000-0003-0255-8422
dc.authoridUymaz, Sait Ali/0000-0003-2748-8483
dc.contributor.authorKocer, Havva Gul
dc.contributor.authorTurkoglu, Bahaeddin
dc.contributor.authorUymaz, Sait Ali
dc.date.accessioned2024-11-07T13:32:36Z
dc.date.available2024-11-07T13:32:36Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractBiological systems where order arises from disorder inspires for many metaheuristic optimization techniques. Self-organization and evolution are the common behaviour of chaos and optimization algorithms. Chaos can be defined as an ordered state of disorder that is hypersensitive to initial conditions. Therefore, chaos can help create order out of disorder. In the scope of this work, Golden Ratio Guided Local Search method was improved with inspiration by chaos and named as Chaotic Golden Ratio Guided Local Search (CGRGLS). Chaos is used as a random number generator in the proposed method. The coefficient in the equation for determining adaptive step size was derived from the Singer Chaotic Map. Performance evaluation of the proposed method was done by using CGRGLS in the local search part of MLSHADE-SPA algorithm. The experimental studies carried out with the electroencephalographic signal decomposition based optimization problems, named as Big Data optimization problem (Big-Opt), introduced at the Congress on Evolutionary Computing Big Data Competition (CEC'2015). Experimental results have shown that the local search method developed using chaotic maps has an effect that increases the performance of the algorithm.& COPY; 2023 Karabuk University. Publishing services by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
dc.description.sponsorshipCoordinatorship of Scientific Research Projects of Seluk University [18101012]
dc.description.sponsorshipAcknowledgments This work was supported by The Coordinatorship of Scientific Research Projects of Selcuk University [Grant number: 18101012] .
dc.identifier.doi10.1016/j.jestch.2023.101388
dc.identifier.issn2215-0986
dc.identifier.scopus2-s2.0-85150790243
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jestch.2023.101388
dc.identifier.urihttps://hdl.handle.net/11480/15507
dc.identifier.volume41
dc.identifier.wosWOS:001026467100001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier - Division Reed Elsevier India Pvt Ltd
dc.relation.ispartofEngineering Science and Technology-An International Journal-Jestech
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectBig Data Optimization
dc.subjectLocal search
dc.subjectGolden ratio
dc.subjectChaotic map
dc.subjectMemetic algorithm
dc.titleChaotic golden ratio guided local search for big data optimization
dc.typeArticle

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