A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems

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dc.authoridDuman, Serhat/0000-0002-1091-125X
dc.authoridKAHRAMAN, Hamdi Tolga/0000-0001-9985-6324
dc.authoridsonmez, yusuf/0000-0002-9775-9835
dc.authoridguvenc, ugur/0000-0002-5193-7990
dc.authorwosidDuman, Serhat/O-9406-2014
dc.authorwosidKAHRAMAN, Hamdi Tolga/AAW-5335-2020
dc.authorwosidguvenc, ugur/H-3029-2011
dc.authorwosidsonmez, yusuf/J-4733-2014
dc.contributor.authorDuman, Serhat
dc.contributor.authorKahraman, Hamdi Tolga
dc.contributor.authorSönmez, Yusuf
dc.contributor.authorGüvenç, Uğur
dc.contributor.authorKati, Mehmet
dc.contributor.authorAras, Sefa
dc.date.accessioned2023-07-26T11:51:09Z
dc.date.available2023-07-26T11:51:09Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractThe teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based metaheuristic search algorithm. It combines the exploitation of the teaching learning-based optimization (TLBO) with the exploration of the artificial bee colony (ABC). With the hybridization of these two nature-inspired swarm intelligence algorithms, a robust method has been proposed to solve global optimization problems. However, as with swarm-based algorithms, with the TLABC method, it is a great challenge to effectively simulate the selection process. Fitness-distance balance (FDB) is a powerful recently developed method to effectively imitate the selection process in nature. In this study, the three search phases of the TLABC algorithm were redesigned using the FDB method. In this way, the FDB-TLABC algorithm, which imitates nature more effectively and has a robust search performance, was developed. To investigate the exploitation, exploration, and balanced search capabilities of the proposed algorithm, it was tested on standard and complex benchmark suites (Classic, IEEE CEC 2014, IEEE CEC 2017, and IEEE CEC 2020). In order to verify the performance of the proposed FDB-TLABC for global optimization problems and in the photovoltaic parameter estimation problem (a constrained real-world engineering problem) a very comprehensive and qualified experimental study was carried out according to IEEE CEC standards. Statistical analysis results confirmed that the proposed FDB-TLABC provided the best optimum solution and yielded a superior performance compared to other optimization methods.en_US
dc.identifier.doi10.1016/j.engappai.2022.104763
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85126133990en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.engappai.2022.104763
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12504
dc.identifier.volume111en_US
dc.identifier.wosWOS:000793829900002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGürel, Ali Etem
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectFitness-Distance Balance (Fdb); Teaching-Learning-Based Artificial Bee Colony; Solar Cell; Parameter Estimation; Pv Modelingen_US
dc.subjectArtificial Bee Colony; Particle Swarm Optimization; Learning-Based Optimization; Cell Models; Differential Evolution; Harmony Search; Hybrid; Identification; Extraction; Explorationen_US
dc.titleA powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problemsen_US
dc.typeArticleen_US

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