湖南大学学报（自然科学版）2011，Vol.38Issue(1)：84-88,5.
一种基于双子群的改进粒子群优化算法
An Improved Particle Swarm Optimization Algorithm Based on Two-subpopulation
摘要
Abstract
Particle Swarm Optimization algorithm easily gets stuck at local optimal solution and shows premature convergence.An improved Particle Swarm Optimization algorithm based on two-subpopulation(TS-IPSO) was proposed.The search range of the algorithm was extended through main subpopulation particle swarm and assistant subpopulation particle swarm, whose search direction was inversed completely.It also adopts the crossbreeding mechanism in genetic algorithm, and uses non-linear inertia weight reduction strategy to accelerate the optimization convergence and improve the search capabilities of particles, then effectively decrease the risk of trapping into local optima.Experiment results have shown that the TS-IPSO can greatly improve the global convergence ability and enhance the rate of convergence, compared with SPSO.关键词
收敛性/粒子群优化算法/子群/杂交机制/遗传算法Key words
convergence/ Particle Swarm Optimization (PSO) algorithm/ subpopulation/ crossbreeding/ genetic arithmetic分类
信息技术与安全科学引用本文复制引用
张英杰,李亮,张英豪,罗春松..一种基于双子群的改进粒子群优化算法[J].湖南大学学报（自然科学版）,2011,38(1):84-88,5.基金项目
国家自然科学基金资助项目(60634020) （60634020）
湖南省科技计划重点资助项目(2010GK2022) （2010GK2022）
