Imperialist Competitive Algorithm with Effective Assimilation Strategy: A Comparative Study on Numerical Benchmark Functions
Elnaz Davoodi, Ebrahim Babaei, Behnam Mohammadi‐Ivatloo · IETE Journal of Research · 2018
In recent years, the nature-inspired optimization algorithms known as intelligent optimization methods have been applied successfully for solving the different problems, along with the well-known mathematical methods. One of the new evolutionary algorithms presented lately is the imperialist competitive algorithm (ICA). This algorithm is based on the behaviour of imperialists in their attempt to conquer the colonies. In this paper, the original ICA has been extended and a new version of ICA has been proposed entitled “MICA”. The proposed MICA uses an efficient assimilation strategy to enhance the global exploration ability and to preserve a premature convergence. This new assimilation scheme uses the most powerful imperialist’s information to update. To validate the efficiency of the proposed algorithm, MICA is tested on a set of 28 non-linear benchmark functions with various dimensions and complexities. The results demonstrate that the proposed strategy enables the modified ICA to have better or at least comparable outcomes in comparison with the original ICA and the other state-of-the-art approaches at handing different types of problems.