带局部搜索的动态多群体自适应差分进化算法及函数优化

 提出将一种改进的差分进化算法——带局部搜索的动态多群体自适应差分进化算法(DMSDELS)应用于函数优化.该算法将种群中的个体随机动态分成多个子群体,以增强个体间的信息交换;变异操作中,选择最优个体为基向量,差分向量的方向选择有利于搜索的方向,以提高收敛速度;变异尺度因子F与交叉概率CR采用自适应机制,以平衡局部搜索与全局搜索;部分优秀个体搜索达到指定代数进入局部搜索,以加快收敛.通过对13个benchmark典型复杂函数进行测试,并与其他七种优化算法进行比较,仿真结果表明:DMSDELS算法具有较高的搜索精度和收敛性,且具有较强的跳出局部最优解能力.

Dynamic Multi-group Self-adaptive Differential Evolution Algorithm with Local Search for Function Optimization

An improved algorithm based on differential evolution algorithms,dynamic multi-group self-adaptive differential evolution algorithm with local search (DMSDELS),is applied to optimize functions in this paper.In DMSDELS,the population is randomly and dynamically divided into multi-group individuals,which can exchange information.To speed up search,in the mutation phase the best individual is chosen as the base vector,and the selection of the direction for difference vector is benefit to search.The scaling factor F and the crossover rate CR are self-adapted in order to balance the local search and the global search.To accelerate the convergence,elitist individuals could search in local after they explored specified generations.DMSDELS is tested on thirteen complex benchmark functions.The results are compared with those of other seven algorithms.The results show that DMSDELS is better in the search precision,convergence property and has strong ability to escape from the local sub-optima.