计算机工程2012，Vol.38Issue(13)：182-184,3.DOI:10.3969/j.issn.1000-3428.2012.13.054
一种群活性反馈粒子群优化算法
Particle Swarm Optimization Algorithm with Swarm Activity Feedback
摘要
Abstract
Aiming at the premature convergence problem in Particle Swarm Optimization(PSO) algorithm, a new evolutionary PSO algorithm with Swarm Activity Feedback(SAF-PSO) is proposed. The method uses swarm activity as diversity index. When swarm activity is quickened to descend, the evolution or mutation operation are added to the iterative process to modify the positions or velocities of particles in order to increase the ability of algorithm to break away from the local optimum and to find the global optimum is greatly improved. Experimental results on several benchmark functions and the comparison with other algorithms show that SAF-PSO has strong global search ability and high accuracy.关键词
粒子群优化/群活性/进化/变异/全局搜索Key words
Particle Swarm Optimization(PSO)/ swarm activity/ evolution/ mutation/ global search分类
信息技术与安全科学引用本文复制引用
左旭坤,苏守宝..一种群活性反馈粒子群优化算法[J].计算机工程,2012,38(13):182-184,3.基金项目
国家自然科学基金资助项目(61075049) （61075049）
安徽省高校自然科学研究基金资助项目(KJ2010B467) （KJ2010B467）
