Fuzzy adaptive imperialist competitive algorithm for global optimization

Most of the parameters in meta-heuristic algorithms need precise tuning before applying into the optimization problems. The quality of solutions obtained by meta-heuristic algorithms is highly dependent on how efficiently its parameters are tuned by the users. Setting the parameters is not straightforward, and there is no definitive way to determine their appropriate values other than trial and error, which is an inefficient and time-consuming process. Deviation angle parameter in the imperialist competitive algorithm (ICA) plays an important role as driver of search diversification and intensification. In this paper, we propose a fuzzy adaptive imperialist competitive algorithm (FAICA), where the deviation parameter is adaptively adjusted using a fuzzy controller to optimally control diversification and intensification through the search process. A set of benchmark function tests has been carried out to demonstrate the effectiveness and robustness of the proposed algorithm. The results of experiments are compared with the original ICA and other five well-known algorithms. The experimental results exhibit the merit of achieving balance between intensification and diversification in the search which indicates superiority of FAICA over ICA.