关键词: 差分进化, 维度层, 加权异维学习, 种群聚集度, 自适应变异

Abstract: In order to solve the problem of differential evolution algorithm, such as premature convergence, slow convergence speed and low convergence precision, a differential evolution algorithm based on adaptive mutation operator is proposed. In this paper, the definition of individual vector particle and dimensional layer is presented. Based on the different dimension’s selection strategy for weighted dimensional layer, the weighted different dimensional learning is introduced into differential evolution algorithm for the first time, which can effectively improve the diversity of the population. According to the degree of populational aggregation, and an adaptive mutation operator based on degree of populational aggregation is proposed. The operator can adaptively adjust the variation weight of DE/best/1 mutation operator and the different dimensional learning mutation operator according to the degree of populational aggregation currently. It accelerates the convergence speed, improves the convergence precision of the algorithm. 20 typical test functions are tested, the results show that compared with the 7 representative algorithms, the algorithm proposed in this paper has great advantages in solving accuracy and convergence speed, and it shows very good robustness.

Key words: differential evolution, dimensional layer, weighted different dimensional learning, degree of populational aggregation, adaptive mutation operator