Duman, SerhatKahraman, Hamdi TolgaSönmez, YusufGüvenç, UğurKati, MehmetAras, Sefa2023-07-262023-07-2620220952-19761873-6769https://doi.org/10.1016/j.engappai.2022.104763https://hdl.handle.net/20.500.12684/12504The 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.en10.1016/j.engappai.2022.104763info:eu-repo/semantics/closedAccessFitness-Distance Balance (Fdb); Teaching-Learning-Based Artificial Bee Colony; Solar Cell; Parameter Estimation; Pv ModelingArtificial Bee Colony; Particle Swarm Optimization; Learning-Based Optimization; Cell Models; Differential Evolution; Harmony Search; Hybrid; Identification; Extraction; ExplorationArticle1112-s2.0-85126133990WOS:000793829900002Q1Q1