计算机工程与应用2011，Vol.47Issue(16)：32-34,3.DOI:10.3778/j.issn.1002-8331.2011.16.010
新型的动态粒子群优化算法
Novel particle swarm optimization algorithm
摘要
Abstract
To solve the problem that adaptive particle swarm algorithm with dynamically changing inertia weigh algorithm is apt to trap in local optimum,a dynamic particle swarm optimization algorithm with adaptive mutation is proposed.The adaptive learning factor and adaptive mutation strategy are introduced in this new algorithm, so that proposed algorithm can easily jump out of local optimum with effective dynamic adaptability.The test experiments with three well-known benchmark functions show that the convergence speed of proposed algorithm is significantly superior to existing algorithms,and the convergence accuracy of algorithm is also increased.关键词
粒子群优化算法/惯性权重/自适应变异/学习因子Key words
particle swarm optimization algorithm/ inertia weight/ adaptive mutation/ learning factor分类
信息技术与安全科学引用本文复制引用
王润芳,张耀军,裴志松..新型的动态粒子群优化算法[J].计算机工程与应用,2011,47(16):32-34,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.70701016). （the National Natural Science Foundation of China under Grant No.70701016）
