南京理工大学学报（自然科学版）2012，Vol.36Issue(1)：31-36,6.
一种双种群遗传粒子群算法及在SMB优化中的应用
Double Populations Genetic and Particle Swarm Algorithm and Its Application in SMB Optimization
摘要
Abstract
Considering the limitation of genetic algorithm ( GA ) and particle swarm optimization (PSO) ,a multi-objective double populations algorithm based on Pareto non-inferior solutions set is presented. Two single populations are set to search optimization respectively. According to a certain proportion, good individuals are selected to exchange every certain generations. Two single populations continue to optimize seperately. The two populations are all convergent to Pareto optimal front. Multi-objective optimization of the operational conditions for the simulated moving bed(SMB)is designed, which sets separation purity and performance as constraint condition and objective function respectively. The results show that the double populations' algorithm is better in convergence and distribution. The optimized operating conditions are effective to improve the separation performance.关键词
模拟移动床/动态模型/多目标优化/遗传算法/粒子群算法/双种群Key words
simulated moving bed/dynamic models/multi-objective optimization/genetic algorithm/ particle swarm optimization/ double populations分类
信息技术与安全科学引用本文复制引用
肖迪,葛启承,林锦国,程明..一种双种群遗传粒子群算法及在SMB优化中的应用[J].南京理工大学学报（自然科学版）,2012,36(1):31-36,6.基金项目
国家"863"计划资助项目(2009AA04Z161) （2009AA04Z161）
