A novel parallel differential evolution (NPDE) algorithm with dynamically adjusting the number of subpopulation individuals was proposed for solving complex optimization problems.
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。
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.
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。
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