On the Convergence of Imperialist Competitive Algorithm
Julie Yu-Chih Liu, Chung Yen Su, Chiang-Tien Chiu · 2013
Evolutionary algorithms have proved to be a powerful tool for solving complex optimization problems. Imperialist Competitive Algorithm (ICA) is a new evolutionary algorithm. Although ICA has been widely applied to solve many engineering problems, the convergence behavior of ICA is rarely discussed. This paper studies how ICA's parameters affect its convergence behavior. Our results indicate that one can choose the parameters' values intelligently to improve the exploration ability of ICA and still guarantee its convergence. Moreover, the results suggest the possibility of adaptive ICA that adjusts its parameters' values dynamically to meet the need of diversity and convergence in the course of its execution.