动态分级的并行约束优化进化算法

提出一种动态分级的并行进化算法用于求解约束优化问题。该算法首先利用佳点集方法初始化种群。在进化过程中，将种群个体分为两个子种群，分别用于全局和局部搜索，并根据不同的搜索阶段动态调整各种级别中并行变量的数目。标准测试问题的实验结果表明了该算法的可行性和有效性。

Dynamic hierarchical parallel constrained optimization evolutionary algorithm

This paper proposed dynamic hierarchical parallel evolutionary algorithm for solving constrained optimization problems. In this approach, introduced the individuals generation based on good-point-set method into the evolutionary algorithm initial step. During the evolution process, divided the initial population into two subpopulations, which were employed for global and local search respectively. Hierarchical ways of parallel variables were dynamically adapted according to the different search phases. The proposed algorithm was tested on six well-known constrained optimization problems, and the experiment results show the feasibility and effectiveness of the method.