IEGQO-AOA: Information-Exchanged Gaussian Arithmetic Optimization Algorithm with Quasi-opposition learning

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dc.contributor.authorÇelik, Emre
dc.date.accessioned2023-07-26T11:49:54Z
dc.date.available2023-07-26T11:49:54Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractArithmetic optimization algorithm (AOA) is a math optimizer proposed to solve optimization chal-lenges. Its capability to find the global solution comes from the behavior of four arithmetic operators: multiplication, division, subtraction and addition. Local minima stagnation and sluggish convergence are the major concerns of AOA. To handle these issues, three effective modifications are proposed. Information exchange is introduced among the search agents first. Then, promising solutions around the best and current solutions are visited by a plausible way based on the Gaussian distribution. Finally, quasi-opposition of the best solution is obtained to have a higher chance of approaching the global solution. The proposed approach is named as Information-Exchanged Gaussian AOA with Quasi-Opposition learning (IEGQO-AOA). 23 standard benchmark functions, 10 CEC2020 test functions and 1 real-life engineering design problem are solved by the proposed IEGQO-AOA and its competing peers such as the original and modified versions of AOA, dwarf mongoose optimization, reptile search algorithm, aquila optimizer, bat algorithm, sine cosine algorithm, original and enhanced version of salp swarm algorithm, dragonfly search algorithm, LSHADE-EpSin, stochastic fractal search, improved jaya and moth-flame optimization, perturbed stochastic fractal search and nelder-mead simplex orthogonal learning moth-flame optimization algorithm. Comparative results based on the statistical tests ratify the potential of IEGQO-AOA in solving problems concerning accuracy and convergence without compromising on the algorithm's simplicity much.(c) 2022 Elsevier B.V. All rights reserved.en_US
dc.identifier.doi10.1016/j.knosys.2022.110169
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85145252082en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2022.110169
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12157
dc.identifier.volume260en_US
dc.identifier.wosWOS:000906810200001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇelik, Emre
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectArithmetic Optimization Algorithm; Information Exchange; Gaussian Distribution; Quasi-Opposition Learning; Metaheuristic; Optimizationen_US
dc.subjectSymbiotic Organisms Search; Automatic-Generation Control; Stochastic Fractal Search; Controller; Designen_US
dc.titleIEGQO-AOA: Information-Exchanged Gaussian Arithmetic Optimization Algorithm with Quasi-opposition learningen_US
dc.typeArticleen_US

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