计算机工程与应用2011，Vol.47Issue(33)：46-48,3.DOI:10.3778/j.issn.1002-8331.2011.33.013
基于受控混沌映射的简化粒子群优化算法
Simplified particle swarm optimization based on controlled chaotic mapping
摘要
Abstract
A new Particle Swarm Optimization(PSO) algorithm is proposed based on three aspects of improvement in standard PSO to solve the problems about premature convergence and low precision.The iteration formula of PSO based on the simple PSO which removes the velocity parameter is applied.Inertia weight,an important factor in PSO,is determined using a controlled chaotic variable to enhance the balance of global and local search of algorithm.The mutation operators are introduced to adjust individual and global optimal to improve the search performance of algorithm.The simulation experiments show that the proposed algorithm not only has great advantages of convergence property over standard PSO and some other modified PSO algorithms, but also effectively avoids being trapped in local minima.关键词
粒子群优化算法/混沌/惯性权重/变异Key words
Particle Swarm Optimization(PSO)/chaos/inertia weight/mutation分类
信息技术与安全科学引用本文复制引用
赵志刚,张福刚,张振文..基于受控混沌映射的简化粒子群优化算法[J].计算机工程与应用,2011,47(33):46-48,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.61063031). （the National Natural Science Foundation of China under Grant No.61063031）
