A novel hyper-heuristic algorithm: an application to automatic voltage regulator

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dc.contributor.authorHinislioglu, Yunus
dc.contributor.authorGüvenç, Uǧur
dc.date.accessioned2025-10-11T20:45:19Z
dc.date.available2025-10-11T20:45:19Z
dc.date.issued2024
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThis paper presents a novel optimization algorithm called hyper-heuristic fitness-distance balance success-history-based adaptive differential evolution (HH-FDB-SHADE). The hyper-heuristic algorithms have two main structures: a hyper-selection framework and a low-level heuristic (LLH) pool. In the proposed algorithm, the FDB method is preferred as a high-level selection framework to evaluate the LLH pool algorithms. In addition, a total of 10 different strategies is derived from five mutation operators and two crossover methods for using them as the LLH pool. Balancing the exploration and exploitation capability of FDB is the main reason for being the selection framework of the proposed algorithm. The success of the HH-FDB-SHADE algorithm was tested on CEC-17 and CEC-20 benchmark test suits for different dimensional search spaces, and the obtained solutions from the HH-FDB-SHADE were compared to 10 different LLH pool algorithms. In addition, the HH-FDB-SHADE algorithm has been applied to optimize the control parameters of PID, PIDF, FOPID, and PIDD2 in the optimal automatic voltage regulator (AVR) design problem to reveal the improved algorithm's performance more clearly and prove its success in solving engineering problems. The results obtained from the AVR system are compared with five other effective meta-heuristic search algorithms such as the fitness-distance balance Lévy Flight distribution, differential evolution, Harris–Hawks optimization, Barnacles mating optimizer, and Moth–Flame optimization algorithms in the literature. The results of the statistical analyses indicate that HH-FDB-SHADE is the best-ranked algorithm for solving CEC-17 and CEC-20 benchmark problems and gives better results compared to the LLH pool algorithms. Besides, the proposed algorithm is more effective and robust than five other meta-heuristic algorithms in solving optimal AVR design problems. © 2024 Elsevier B.V., All rights reserved.en_US
dc.identifier.doi10.1007/s00521-024-10313-z
dc.identifier.endpage21364en_US
dc.identifier.issn1433-3058
dc.identifier.issn0941-0643
dc.identifier.issue34en_US
dc.identifier.scopus2-s2.0-85202152318en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage21321en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-024-10313-z
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21270
dc.identifier.volume36en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzKA_Scopus_20250911
dc.subjectAutomatic Voltage Regulatoren_US
dc.subjectFitness-distance Balanceen_US
dc.subjectHyper-heuristic Searchen_US
dc.subjectSuccess History-based Adaptive Differential Evolutionen_US
dc.subjectBenchmarkingen_US
dc.subjectHeuristic Methodsen_US
dc.subjectProportional Control Systemsen_US
dc.subjectRobust Controlen_US
dc.subjectThree Term Control Systemsen_US
dc.subjectAutomatic Voltage Regulatoren_US
dc.subjectDifferential Evolutionen_US
dc.subjectDistance Balanceen_US
dc.subjectFitness-distance Balanceen_US
dc.subjectHeuristic Searchen_US
dc.subjectHyper-heuristic Searchen_US
dc.subjectHyper-heuristicsen_US
dc.subjectSelection Frameworken_US
dc.subjectSuccess History-based Adaptive Differential Evolutionen_US
dc.subjectVoltage Regulator'sen_US
dc.subjectHeuristic Algorithmsen_US
dc.titleA novel hyper-heuristic algorithm: an application to automatic voltage regulatoren_US
dc.typeArticleen_US

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