计算机应用研究2012，Vol.29Issue(7)：2429-2431,3.DOI:10.3969/j.issn.1001-3695.2012.07.007
一种改进的动态多种群并行差分进化算法
Dynamic multi-species parallel differential evolution algorithm
摘要
Abstract
Aiming at the problems of single population getting into premature convergence easily, this paper proposed a novel dynamic multi-population differential evolution algorithm. In this approach, it introduced the good point set method into the differential evolution (DE) initial step, which reinforces the stability and global exploration ability of the DE algorithm. Du-ring ihe evolution process, the proposed algorithm was based on individual fitness values, and divided the initial population in-to three sub-populations, and then they evolved with different DE algorithm by several trial vector generation strategies with a number of control parameter settings. It not only kept the independence of the sub-population and the superiority of the opera-tors , but also not increased the complexity of algorithm. Tasted four classic benchmarks problems, and the experiment results show that the proposed algorithm is an effective method for different optimization problems.关键词
多种群/差分进化算法/并行/佳点集方法Key words
multiple subpopulation/ differential evolution algorithm/ parallel/ good point set method分类
信息技术与安全科学引用本文复制引用
龙文..一种改进的动态多种群并行差分进化算法[J].计算机应用研究,2012,29(7):2429-2431,3.基金项目
国家自然科学基金资助项目(61074069) （61074069）
贵州财经大学引进人才科研启动资助项目 （）
