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Abstract
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The supreme council of a city is usually formed by the evolution of councils from the smallest neighborhoods to the largest ones, regions, and finally the whole city. Council members of a region try to improve their performance to be selected as the boss of the council in the future election, and also a member of the larger region council. This fact motivates us to propose a socio-inspired metaheuristic optimization algorithm (named as City Councils Evolution (CCE)) inspired by the evolution of city councils. To analyze the effectiveness of CCE, it is applied to solve 20 general test functions and 29 benchmark functions from CEC 2017. Results of CCE are compared with the effectiveness of nine popular and new optimization algorithms belonging to different classes: SHADE and LSHADE-cnEpSin as optimization algorithms with high performance and winners of IEEE CEC competitions (2013 and 2017), and EO, BWO, PO, BMO, CHOA, AO, and WHO as newly developed algorithms (2020 and 2021). According to the average rank of Friedman test, for all 49 test functions, CCE outperforms EO, BWO, PO, BMO, CHOA, AO, and WHO by 65%, 95%, 64%, 68%, 80%, 74%, and 71%, respectively, whereas it is outperformed by SHADE and LSHADE-cnEpSin by 49% and 65%, respectively. Finally, the obtained results of solving real-world constrained optimization problems by the proposed algorithm show that it has better performance compared to some good algorithms in the literature. The source code of the CCE algorithm is publicly available at https://github.com/EinPira/City-Councils-Evolution-Algorithm.
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