A new differential evolution algorithm with a hybrid mutation operator and self-adapting control parameters for global optimization problems

The differential evolution (DE) algorithm is a notably powerful evolutionary algorithm that has been applied in many areas. Therefore, the question of how to improve the algorithm’s performance has attracted considerable attention from researchers. The mutation operator largely impacts the performance of the DE algorithm The control parameters also have a significant influence on the performance. However, it is not an easy task to set a suitable control parameter for DE. One good method is to considering the mutation operator and control parameters simultaneously. Thus, this paper proposes a new DE algorithm with a hybrid mutation operator and self-adapting control parameters. To enhance the searching ability of the DE algorithm, the proposed method categorizes the population into two parts to process different types of mutation operators and self-adapting control parameters embedded in the proposed algorithm framework. Two famous benchmark sets (including 46 functions) are used to evaluate the performance of the proposed algorithm and comparisons with various other DE variants previously reported in the literature have also been conducted. Experimental results and statistical analysis indicate that the proposed algorithm has good performance on these functions.