University of Tabriz Tabriz Journal of Electrical Engineering 2008-7799 55 1 2025 05 22 Integrating Exchange Market, Queen Bee, and Shuffled Complex Evolution Algorithms for Multi-variable Function Optimization Integrating Exchange Market, Queen Bee, and Shuffled Complex Evolution Algorithms for Multi-variable Function Optimization 177 188 17762 10.22034/tjee.2024.57954.4689 FA Mina Salim Department of Control Engineering, Faculty of Electrical and Computer Engineering, university of Tabriz Sima Hamedifar Department of Electronics, Carleton University, Ottawa, Canada Ali Asghar Lotfi Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran Journal Article 2023 08 11 In this paper, three popular algorithms, including the Exchange Market Algorithm (EMA), the Shuffled Complex Evolution (SCE) algorithm, and the Queen Bee (QB) algorithm, are considered to propose three new hybrid evolutionary algorithms named EMA-QB, EMA-SCE, and EMA-SCE-QB. Then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of EMA, SCE, and QB algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. It is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. Finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms. In this paper, three popular algorithms, including the Exchange Market Algorithm (EMA), the Shuffled Complex Evolution (SCE) algorithm, and the Queen Bee (QB) algorithm, are considered to propose three new hybrid evolutionary algorithms named EMA-QB, EMA-SCE, and EMA-SCE-QB. Then, to analyze and validate the effectiveness and efficiency of these new algorithms, we compared their performance with the performance of EMA, SCE, and QB algorithms on 12 benchmark functions with 10, 20, 30, and 50 variables. It is deduced that hybridization has presented a better performance in optimum seeking from both time and accuracy points of view, which become more distinctive as the number of variables grows. Finally, the sum of run times, minimum value of cost functions, and the number of iterations obtained from the procedure of optimization of all functions using the considered algorithms are illustrated in four graphs for each number of variables, which prove the success of the proposed hybrid algorithms. https://tjee.tabrizu.ac.ir/article_17762_b0edb687736761357b0b9d393ce6ebe9.pdf