Publication:
Gozde: A novel metaheuristic algorithm for global optimization

dc.contributor.authorKuyu, Yiğit Çağatay
dc.contributor.authorVatansever, Fahri
dc.contributor.buuauthorKUYU, YİĞİT ÇAĞATAY
dc.contributor.buuauthorVATANSEVER, FAHRİ
dc.contributor.departmentElektrik Elektronik Mühendisliği Bölümü
dc.contributor.orcid0000-0002-3885-8622
dc.contributor.researcheridAAG-8425-2021
dc.contributor.researcheridAAC-6923-2021
dc.date.accessioned2024-11-12T10:06:43Z
dc.date.available2024-11-12T10:06:43Z
dc.date.issued2022-11-01
dc.description.abstractThis study proposes a new metaheuristic algorithm, called "Geometric Octal Zones Distance Estimation "(GOZDE) algorithm to solve global optimization problems. The presented GOZDE employs a search scheme with the information sharing between the zones considering the distance of the zones utilizing median values. The whole population represents the eight zones that are the combination of different search strategies to guide knowledge dissemination from one zone to others in the search space. To demonstrate the effectiveness of the proposed optimizer, it is compared with two classes of metaheuristics, which are (1) GA, PSO, DE, CS and HS as the classical metaheuristics and (2) BWO, SSA, MVO, HHO, ChOA, AOA and EBOwithCMAR as the up-to-date metaheuristics. The search capability of the proposed algorithm is tested on two different numerical benchmark sets including low and high dimensional problems. The developed algorithm is also adapted to ten real world applications to handle constraint optimization problems. In addition, to further analyse the results of the proposed algorithm, three well-known statistical metrics, Friedman, Wilcoxon rank sum and Whisker-Box statistical tests are conducted. The experimental results statistically show that GOZDE is significantly better than, or at least comparable to the twelve metaheuristic algorithms with outstanding performance in solving numerical functions and real-world optimization problems. (C) 2022 Elsevier B.V. All rights reserved.
dc.identifier.doi10.1016/j.future.2022.05.022
dc.identifier.eissn1872-7115
dc.identifier.endpage152
dc.identifier.issn0167-739X
dc.identifier.startpage128
dc.identifier.urihttps://doi.org/10.1016/j.future.2022.05.022
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0167739X22001947
dc.identifier.urihttps://hdl.handle.net/11452/47759
dc.identifier.volume136
dc.identifier.wos000890487100009
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherElsevier
dc.relation.journalFuture Generation Computer Systems-the International Journal of Escience
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEvolutionary algorithms
dc.subjectSystems
dc.subjectDesign
dc.subjectEvolutionary computation
dc.subjectGlobal optimization
dc.subjectReal-world problems
dc.subjectMetaheuristic algorithms
dc.subjectPopulation-based method
dc.subjectGozde
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectComputer science, theory & methods
dc.titleGozde: A novel metaheuristic algorithm for global optimization
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentElektrik Elektronik Mühendisliği Bölümü
relation.isAuthorOfPublication04fc60e2-d4a3-4614-b912-4d7d5e1ab573
relation.isAuthorOfPublication32f35813-c6bd-451c-91eb-73aec5e99b0b
relation.isAuthorOfPublication.latestForDiscovery04fc60e2-d4a3-4614-b912-4d7d5e1ab573

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