关键词: 差分进化, t分布, 进化策略自适应, 交叉概率自适应

Abstract: A novel differential evolution algorithm is proposed to improve the exploration and exploitation capabilities of differential evolution（DE）. In this algorithm, a new mutation operator following the t distribution is used to integrate the advantages of Gaussian and Cauchy mutation, while both the mutation strategy and crossover probability can be gradually self-adapted by learning from their previous successful experience. Experimental studies are carried out on four classical Benchmark functions, and the computational results show that the algorithm has fast convergence, high accuracy more robustness.

Key words: differential evolution, t distribution, mutation strategy adaptation, crossover probability adaptation