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https://hdl.handle.net/20.500.13091/4333
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DC Field | Value | Language |
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dc.contributor.author | Koçer, Havva Gül | - |
dc.contributor.author | Türkoğlu, Bahaeddin | - |
dc.contributor.author | Uymaz, Sait Ali | - |
dc.date.accessioned | 2023-08-03T19:00:12Z | - |
dc.date.available | 2023-08-03T19:00:12Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2215-0986 | - |
dc.identifier.uri | https://doi.org/10.1016/j.jestch.2023.101388 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/4333 | - |
dc.description.abstract | Biological 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/). | en_US |
dc.description.sponsorship | Coordinatorship of Scientific Research Projects of Seluk University [18101012] | en_US |
dc.description.sponsorship | Acknowledgments This work was supported by The Coordinatorship of Scientific Research Projects of Selcuk University [Grant number: 18101012] . | en_US |
dc.language.iso | en | en_US |
dc.publisher | Elsevier - Division Reed Elsevier India Pvt Ltd | en_US |
dc.relation.ispartof | Engineering Science and Technology-An International Journal-Jestech | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Big Data Optimization | en_US |
dc.subject | Local search | en_US |
dc.subject | Golden ratio | en_US |
dc.subject | Chaotic map | en_US |
dc.subject | Memetic algorithm | en_US |
dc.subject | Differential Evolution Algorithm | en_US |
dc.subject | Cooperative Coevolution | en_US |
dc.subject | Framework | en_US |
dc.title | Chaotic golden ratio guided local search for big data optimization | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.jestch.2023.101388 | - |
dc.identifier.scopus | 2-s2.0-85150790243 | en_US |
dc.department | KTÜN | en_US |
dc.authorid | Turkoglu, Bahaeddin/0000-0003-0255-8422 | - |
dc.identifier.volume | 41 | en_US |
dc.identifier.wos | WOS:001026467100001 | en_US |
dc.institutionauthor | … | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.identifier.scopusquality | Q1 | - |
item.cerifentitytype | Publications | - |
item.grantfulltext | open | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.fulltext | With Fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
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1-s2.0-S2215098623000654-main.pdf | 1.53 MB | Adobe PDF | View/Open |
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