关键词: 差分进化, Laplace分布, 进化策略自适应, 交叉概率自适应

Abstract: To improve the optimum speed and optimization accuracy of Differential Evolution Algorithm (DEA), an improved DEA was proposed. In this algorithm, a new mutation operator following the Laplace distribution was used during the mutation, and both the mutation strategy and the crossover probability could be gradually self-adapted to fit different phases of evolution by learning from their previous successful experience. Experimental studies were carried out on five classical Benchmark functions, and the computational results show that the algorithm has faster convergence, higher accuracy and stronger robustness, and it is suitable to solve high-dimensional complex global optimization problems.

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