Adaptive Differential Evolution Algorithm Based on Restart Mechanism and Direction Information

Abstract

Differential evolution is a competent algorithm for solving single objective real-parameter optimization problems. In order to enhance the performance of adaptive DE algorithms based on successful parameters, in this paper, a new DE algorithm, called adaptive differential evolution restart and direction, abbreviated ADERD, is proposed for solving global numerical optimization problems over continuous space. In the proposed algorithm, a novel mutation strategy based on the feasible descent direction is introduced. Only individuals of the population top ranked with smaller errors adopt this novel strategy to mutate. Additionally, the variable coefficient is used in the restart mechanism first to avoid stagnation and/or jump out of the local optima. Modified mechanism of crossover probability sorting is also introduced. In order to better understand the effectiveness of our proposed strategies, those are integrated into two representative adaptive DE variants, i.e. JADE_ rcr and JADE_ sort. Experimental results demonstrate that the our proposed strategies are capable of enhancing the performance of JADE_ rcr and JADE_ sort. Improved JADE_ sort is denoted as ADERD_ sort. Experiments have been conducted on 30 functions presented in CEC 2017 competition. Moreover, compared with recent adaptive DE algorithms, ADERD_ sort obtains better, or at least comparable, results in terms of the quality of final solutions.