中文摘要：

为有效提高差分进化（DE）算法的优化性能，提出一种动态多子群差分进化（DMsDE）算法．该算法从种群多样性的角度，提出一种动态多子群策略，以增加算法跳出局部极值的可能性．然后，设计了一种平衡局部搜索和全局搜索的随机引导变异操作。以提高搜索的有效性和广泛性．同时，引入全局最优学习操作，防止算法早熟．最后，与差分进化算法和各种改进的差分进化算法及其他智能优化算法做比较，仿真数值结果表明了DMSDE算法的有效性．

英文摘要：

In order to improve the performance of differential evolution （DE ） algorithm effectively, a dynamic multi-subgroups differ- ential evolution （DMSDE） algorithm is presented. In this algorithm, a dynamic multi-subgroups strategy is proposed from the view of population diversity to add the probability of jumping out local minima. Then, a random guided mutation operator is designed based on a balance between local search and global search, which is aim at enhance the effectiveness and universality of searching. Meanwhile, global best learning operation is introduced to avoid algorithm premature. It is compared with DE algorithm and its improved variants and other intelligent optimization algorithms, the numerical results demonstrated the validity of the proposed DMSDE algorithm.