采用双变异策略的自适应差分进化算法及应用

为了克服差分进化算法早熟收敛和寻优精度低的缺点，提出一种采用双变异策略的自适应差分进化算法（Adaptive Differential Evolution Algorithm using Double mutation strategies，DADE）。DADE引入基于种群相似度和中心解的双变异策略，有效平衡了算法的全局搜索和局部搜索；自适应交叉概率使种群个体向更新成功的个体学习，有利于后续种群的进化。在7个测试函数和3个电力系统动态经济调度（Dynamic Economic Dispatch，DED）问题上的优化结果表明，DADE算法与其他4种DE算法相比具有更强的全局寻优能力，且对电力系统动态经济调度问题的优化结果优于文献中所报道的结果。

Adaptive Differential Evolution Algorithm Using Double Mutation Strategies and Its Application

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.