关键词: 差分演化算法（DE）, 演化模式, 更新策略, 演化计算, 全局优化

Abstract: Differential evolution (DE) is one of the stochastic optimization algorithms, which has advantages of fast convergence speed and good robustness. Currently there are several crossover and mutation strategies for DE, and they exhibit different performance in optimizing different problems. This paper introduces 10 different DE update strategies and tests them on a suit of widely used benchmark problems, in order to thoroughly evaluate and compare their performance. By analyzing and comparing the convergence curves of those DE algorithms in various problem landscapes and optimization states, this paper summarizes the search behavior and performance of these update strategies in different environments. This paper not only provides guidance for engineering application that helps researchers choose a suitable strategy for a given application of DE, but also provides theoretical basis for the design of new update rules and adaptive DE.

Key words: differential evolution (DE), evolutionary variants, update strategies, evolutionary computation, global optimization