关键词: 差分进化算法, 维变异, 扰动, 早熟收敛, 参数调整

Abstract: Because of the problems of Differential Evolution（DE） algorithm such as premature convergence, low accuracy and tedious parameter setting for hard high-dimensional optimization problems, a dynamic differential evolution algorithm with weighted mutation strategy, called WMDDE, is presented. Firstly, two new weighted mutation operators of random disturbance are designed by weighting combination of DE/rand/1 and DE/best/1, which is utilized to balance the global and local search dynamically, and avoid premature convergence. Secondly, a self-adaptive parameter setting strategy of adjust scaling factor and crossover factor is designed, avoiding tedious parameter setting. Finally, experimental results on 11 benchmark functions show that the new algorithm can effectively avoid premature convergence and has the global convergence ability strongly, and its optimization rate solution accuracy, stability are better than the other four kinds of differential evolutions.

Key words: differential evolution algorithm, dimensional mutation, disturbance, premature convergence, parameter setting