Integration search strategies in tree seed algorithm for high dimensional function optimization

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dc.contributor.authorGungor, Imral
dc.contributor.authorEmiroglu, Bulent Gursel
dc.contributor.authorCinar, Ahmet Cevahir
dc.contributor.authorKiran, Mustafa Servet
dc.date.accessioned2021-01-14T18:10:53Z
dc.date.available2021-01-14T18:10:53Z
dc.date.issued2020
dc.departmentKKÜ
dc.descriptionEmiroglu, Bulent Gursel/0000-0002-1656-6450; CINAR, Ahmet Cevahir/0000-0001-5596-6767; Kiran, Mustafa Servet/0000-0002-5896-7180
dc.description.abstractThe tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-dimensional twelve numerical benchmark functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics.en_US
dc.identifier.citationBu makale açık erişimli değildir.en_US
dc.identifier.doi10.1007/s13042-019-00970-1
dc.identifier.endpage267en_US
dc.identifier.issn1868-8071
dc.identifier.issn1868-808X
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85067815829
dc.identifier.scopusqualityQ1
dc.identifier.startpage249en_US
dc.identifier.urihttps://doi.org/10.1007/s13042-019-00970-1
dc.identifier.urihttps://hdl.handle.net/20.500.12587/12812
dc.identifier.volume11en_US
dc.identifier.wosWOS:000512019400002
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSPRINGER HEIDELBERGen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSwarm intelligenceen_US
dc.subjectMetaheuristic algorithmsen_US
dc.subjectWithering processen_US
dc.subjectNonlinear global optimizationen_US
dc.titleIntegration search strategies in tree seed algorithm for high dimensional function optimizationen_US
dc.typeArticle

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