Multi-modal forest optimization algorithm

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dc.contributor.authorOrujpour, Mohanna
dc.contributor.authorFeizi-Derakhshi, Mohammad-Reza
dc.contributor.authorRahkar-Farshi, Taymaz
dc.date.accessioned2021-05-15T11:33:42Z
dc.date.available2021-05-15T11:33:42Z
dc.date.issued2020
dc.departmentMühendislik ve Doğa Bilimleri Fakültesi, Yazılım Mühendisliğien_US
dc.descriptionRahkar-Farshi, Taymaz/0000-0003-4070-1058; Derakhshi, Mohammad Reza Feizi/0000-0002-8548-976X
dc.description.abstractMulti-modal optimization algorithms are one of the most challenging issues in the field of optimization. Most real-world problems have more than one solution; therefore, the potential role of multi-modal optimization algorithms is rather significant. Multi-modal problems consider several global and local optima. Therefore, during the search process, most of the points should be detected by the algorithm. The forest optimization algorithm has been recently introduced as a new evolutionary algorithm with the capability of solving unimodal problems. This paper presents the multi-modal forest optimization algorithm (MMFOA), which is constructed by applying a clustering technique, based on niching methods, to the unimodal forest optimization algorithm. The MMFOA operates by dividing the population of the forest into subpopulations to locate existing local and global optima. Subpopulations are generated by the Basic Sequential Algorithmic Scheme with a radius neighborhood. As population size is self-adaptive in MMFOA, population size can be increased in functions with too many local and global optima. The proposed algorithm is evaluated by a set of multi-modal benchmark functions. The experiment results show that not only is the population size low, but also that the convergence speed is high, and that the algorithm is efficient in solving multi-modal problems.en_US
dc.identifier.doi10.1007/s00521-019-04113-z
dc.identifier.endpage6173en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue10en_US
dc.identifier.scopus2-s2.0-85062769910
dc.identifier.scopusqualityQ1
dc.identifier.startpage6159en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-019-04113-z
dc.identifier.urihttps://hdl.handle.net/20.500.12939/214
dc.identifier.volume32en_US
dc.identifier.wosWOS:000529745200063
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorRahkar-Farshi, Taymaz
dc.language.isoen
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectMulti-Modal Forest Optimization Algorithm (MMFOA)en_US
dc.subjectMulti-Modal Optimizationen_US
dc.subjectNiching Methodsen_US
dc.titleMulti-modal forest optimization algorithm
dc.typeArticle

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