Enhanced equilibrium optimization method with fractional order chaotic and application engineering

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dc.authoridATES, Abdullah/0000-0002-4236-6794
dc.authorwosidATES, Abdullah/V-6929-2018
dc.contributor.authorAtes, Abdullah
dc.date.accessioned2024-08-04T20:49:17Z
dc.date.available2024-08-04T20:49:17Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this study, enhanced equilibrium optimization ((EO)-O-2) algorithm was proposed by developing stochastic processes in equilibrium optimization (EO) method with fractional order chaotic (FOC) system models. FOC model was firstly used in an optimization algorithm in this study. System responses of the fractional order chaotic models were used instead of random coefficients in the basic EO method. The performance of many fractional order chaotic system models was tested on benchmark functions. It was experimentally determined fractional order chaotic models of Genesio Tesi, Chua Memristor and cellular neural network which were convenient for the EO method. Model coefficients and initial conditions of corresponding fractional order chaotic models were obtained for benchmark functions to find suitable models for (EO)-O-2 algorithm. In order to present engineering application performance of the proposed (EO)-O-2 method, controller parameters were optimized for liquid level control that was decoupled two-input and two-output (TITO) tank system. Fractional and integer order PI and PID controllers' parameters were tuned according to the reference input signals for TITO tank system. Multi-objective function was defined with mean square error (MSE) definition as system's overall objective function. Proposed multi-objective function was minimized during to optimization process. (EO)-O-2 algorithm results were compared with each other and existing literature studies results. In this way, it was shown comparatively that usage of fractional order chaotic models in the proposed (EO)-O-2 algorithm affected optimization algorithm performance and produced better results.en_US
dc.identifier.doi10.1007/s00521-021-05756-7
dc.identifier.endpage9876en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue16en_US
dc.identifier.scopus2-s2.0-85100797781en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage9849en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-021-05756-7
dc.identifier.urihttps://hdl.handle.net/11616/99764
dc.identifier.volume33en_US
dc.identifier.wosWOS:000616043800003en_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.subjectEquilibrium optimizationen_US
dc.subjectFractional order chaotic modelen_US
dc.subjectDecouplingen_US
dc.subjectTITOen_US
dc.subjectControlleren_US
dc.subjectBenchmark functionen_US
dc.titleEnhanced equilibrium optimization method with fractional order chaotic and application engineeringen_US
dc.typeArticleen_US

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