动态调整子种群个体的差分进化算法

提出一种新的动态调整子种群个体数目的并行差分进化算法。基于种群个体的适应度值，该算法将种群个体分为三个子种群，分别用于全局搜索、局部搜索及二者的结合。在进化过程中，根据不同的搜索阶段自适应动态调整各子种群个体的数目。另外，不同子种群分别采用不同的变异策略，以协调算法的勘探和开采能力。数值实验结果表明该算法具有较好的寻优效果。

Differential evolution algorithm with dynamically adjusting number of subpopulation individuals

A novel parallel differential evolution (NPDE) algorithm with dynamically adjusting the number of subpopulation individuals was proposed for solving complex optimization problems. In the NPDE algorithm, the initial population was divided into three subpopulations based on the fitness values of individuals, which were employed for global and local search respectively. The number of the subpopulation was dynamically adapted according to the search phases. Different mutation strategies were used to different subpopulation respectively. It coordinated the exploitation ability and the exploration ability of algorithm. Experiments concerning various benchmark functions were designed to test the performance of the NPDE algorithm, and the results show that it can get high performance while dealing with various complex problems.