关键词: 差分进化, 协方差矩阵, 特征坐标系, 多策略变异, 参数自适应

Abstract:

Aiming at the problems of premature convergence and search stagnation in the Differential Evolution（DE） Algorithm, a multi-strategy covariance matrix learning differential evolution algorithm is proposed. Firstly, a feature coordinate system is established through the covariance matrix, and mutation and crossover operations are performed in the feature coordinate system to make full use of the distribution information of the current population and the relationship between the variables to ensure that the population can evolve in the direction of the global optimal solution. The method of selecting mutation strategies based on historical evolution information enables individuals to choose the most suitable mutation strategy at present, increasing the probability of finding the optimal solution. Finally, the adaptation of the cross probability also balances the global exploration ability and local exploration of the algorithm to a certain extent ability. In this paper, the convergence of the algorithm is proved, and the algorithm is simulated on the CEC2017 test set, and the experimental results are compared with other excellent differential evolution algorithms. The comparison results show the effectiveness of the algorithm.

Key words: differential evolution, covariance matrix, feature coordinate system, multi-strategy mutation, parameter adaptation