关键词: 差分进化算法, 双变异策略, 中心解, 自适应交叉概率, 测试函数, 电力系统动态经济调度

Abstract:

To overcome the shortcomings of differential evolution algorithm such as premature convergence and low optimization accuracy, an Adaptive Differential Evolution algorithm using Double mutation strategies（DADE） is presented in this paper. The double mutation strategies based on the population similarity and central solution are introduced in DADE, which effectively balance the global and local searches of the algorithm. The adaptive crossover rate enables the population individuals to learn from the current updated individuals, which is helpful for the evolution of the subsequent population. The optimization results on the seven test functions and three Dynamic Economic Dispatch（DED） problems show that the DADE algorithm has stronger global optimization ability than the other four DE algorithms, and the optimization results on the dynamic economic dispatch problems obtained by the DADE algorithm are better than those reported in the literatures.

Key words: differential evolution algorithm, double mutation strategies, central solution, adaptive crossover rate, test function, dynamic economic dispatch