动态局部搜索差分进化算法

Dynamic Local Search Differential Evolution Algorithm

针对差分进化算法存在进化后期收敛速度慢、易早熟等缺点,提出了一种基于动态局部搜索的差分进化算法(DLSDE).采用随机选择的方式进行变异并运用小概率扰动操作,增加种群的多样性,平衡算法的开发能力和探索能力;同时,对当前的最优解进行动态局部搜索,以加快算法的收敛速度.对标准测试函数进行仿真实验并与其他6种算法进行比较,结果表明DLSDE算法具有较快的收敛速度和较高的求解精度,对复杂的数值优化问题寻优效果很好.

Aiming at the shortcoming of differential evolution （DE）, such as the low convcrgence rate in the late evolution and easy to be trapped into the local optimums, an improved DE algorithm based on local search is proposed in this paper. The random choice method and small probability perturbation are adopted to increase the diversity of the population and to balance exploitation and exploration of the algorithm. Full use is made of dynamic local search （DLS） to optimize the current best solution to speed up the conver- gence rate. Simulation experiments are conducted on a suite of benchmark functions and the results are compared with those of other six algorithms. The results demonstrate that the DLSDE algorithm has afas- ter convergence rate and higher solution accuracy and shows good performance in solving complex numeri cal optimization problems.