Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/4076
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dc.contributor.authorArıcı, Ferda Nur-
dc.contributor.authorKaya, Ersin-
dc.date.accessioned2023-05-30T21:09:05Z-
dc.date.available2023-05-30T21:09:05Z-
dc.date.issued2022-
dc.identifier.issn2822-2385-
dc.identifier.urihttps://doi.org/10.21541/apjess.1065912-
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1123118-
dc.identifier.urihttps://hdl.handle.net/20.500.13091/4076-
dc.description.abstractOptimization is the operation of finding the most appropriate solution for a particular problem or set of problems. In the literature, there are many population-based optimization algorithms for solving optimization problems. Each of these algorithms has different characteristics. Although optimization algorithms give optimum results on some problems, they become insufficient to give optimum results as the problem gets harder and more complex. Many studies have been carried out to improve optimization algorithms to overcome these difficulties in recent years. In this study, six well-known population-based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA, and particle swarm optimization - PSO) were used. Each of these algorithms has its own advantages and disadvantages. These population-based six algorithms were tested on CEC’17 test functions and their performances were examined and so the characteristics of the algorithms were determined. Based on these results, hierarchical approaches have been proposed in order to combine the advantages of algorithms and achieve better results. The hierarchical approach refers to the successful operation of algorithms. In this study, eight approaches were proposed, and performance evaluations of these structures were made on CEC’17 test functions. When the experimental results are examined, it is concluded that some hierarchical approaches can be applied, and some hierarchical approaches surpass the base states of the algorithms.en_US
dc.language.isoenen_US
dc.relation.ispartofAcademic Platform journal of engineering and smart systems (Online)en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPopulation-based Algorithmen_US
dc.subjectOptimizationen_US
dc.subjectCEC’17en_US
dc.subjectHierarchical Approachesen_US
dc.titleHierarchical Approaches to Solve Optimization Problemsen_US
dc.typeArticleen_US
dc.identifier.doi10.21541/apjess.1065912-
dc.departmentKTÜNen_US
dc.identifier.volume10en_US
dc.identifier.issue3en_US
dc.identifier.startpage124en_US
dc.identifier.endpage139en_US
dc.institutionauthor-
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.trdizinid1123118en_US
dc.ktun-updatektunupdateen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairetypeArticle-
item.grantfulltextopen-
item.cerifentitytypePublications-
crisitem.author.dept02.03. Department of Computer Engineering-
Appears in Collections:TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collections
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