Improving the Performance of the City Councils Evolution Algorithm by the Linear Reduction of the Population Size and the Search Space

City Councils Evolution algorithm (CCE) is a metaheuristic algorithm inspired by the formation process of the supreme council of a city due to the formation of councils from the smallest neighborhoods to the largest regions. In this paper, we want to improve the performance of CCE by applying two important changes.The first change is about the continuous reduction of the population size using Linear Population Size Reduction (LPSR) technique. In this technique, the population size in the initial iterations is considered large enough such that it can explore wide areas of search space. As the algorithm progresses, the population size is gradually reduced to increase the convergence speed. The second change is related to the domain of variables, which is constantly reduced to limit the search space, and so the possibility of finding optimal solutions is increased. To evaluate and compare the performance of ICCE with CCE, Chimp Optimization, Black Widow Optimization, Political Optimizer, Barnacles Mating Optimizer, Snake Optimizer, and Aquila Optimizer, we implement them on 29 test functions from 2017 IEEE Congress on Evolutionary Computation (CEC 2017). The results of Friedman mean rank and Wilcoxon signed-rank tests confirm the higher performance of ICCE compared to other algorithms