Fractional-order chaotic oscillator-based Aquila optimization algorithm for maximization of the chaotic with Lorentz oscillator

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dc.authoridAbualigah, Laith/0000-0002-2203-4549
dc.authoridATES, Abdullah/0000-0002-4236-6794
dc.authorwosidAbualigah, Laith/ABC-9695-2020
dc.authorwosidATES, Abdullah/V-6929-2018
dc.contributor.authorCavlak, Yakup
dc.contributor.authorAtes, Abdullah
dc.contributor.authorAbualigah, Laith
dc.contributor.authorElaziz, Mohammed Abd
dc.date.accessioned2024-08-04T20:54:37Z
dc.date.available2024-08-04T20:54:37Z
dc.date.issued2023
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThe random structures in the Aquila optimization algorithm are modeled with fractional chaotic oscillators, and the fractional-order chaotic oscillator-based Aquila optimization (FOCOBAO) algorithm was suggested in this study. First of all, the basic AO algorithm was examined. In particular, random variables that affect the optimization performance of the AO algorithm have been determined. Then, instead of the determined random variables, the coefficients were derived with fractional chaotic oscillators and used in the FOCOBAO. The superiority of the proposed algorithm was primarily demonstrated via twenty-three benchmark functions. The results were matched with GO, EO, GWO, MPA, WOA, SMA and basic AO optimization algorithms. Then, the design of the Lorenz chaotic oscillator, according to maximum chaotic objective function, is a topic that remains up to date in the literature. In this study, a fractional chaotic Lorenz oscillator was designed with FOCOBAO as an engineering application. Especially for maximum chaoticity, maximum positive Lyapunov exponents were determined. In this way, a different design process has been proposed in the literature. The basic AO algorithm, which includes stochastic processes, was developed with fractional chaotic oscillators, and a deterministic method was obtained. The parameters of the Lorenz system were calculated for maximum chaoticity, and the results were presented comparatively.en_US
dc.identifier.doi10.1007/s00521-023-08945-8
dc.identifier.endpage21662en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue29en_US
dc.identifier.scopus2-s2.0-85168354023en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage21645en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-023-08945-8
dc.identifier.urihttps://hdl.handle.net/11616/101523
dc.identifier.volume35en_US
dc.identifier.wosWOS:001051101400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFractional-order chaotic oscillatoren_US
dc.subjectAquila optimization algorithmen_US
dc.subjectLorenz systemen_US
dc.titleFractional-order chaotic oscillator-based Aquila optimization algorithm for maximization of the chaotic with Lorentz oscillatoren_US
dc.typeArticleen_US

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