重庆大学学报2017，Vol.40Issue(5)：95-103,9.DOI:10.11835/j.issn.1000-582X.2017.05.012
改进的粒子群算法及在数值函数优化中应用
Application of improved particle swarm optimization to numerical function optimization
摘要
Abstract
To enhance the optimization ability of the particle swarm optimization (PSO),an improved PSO algorithm was proposed in this paper.In the proposed approach,the Beta distribution function is used to initialize population,and the inverse incomplete gamma function is used to update the inertia weight.For adjustment of velocity,a new operator based on differential evolution is introduced.For cross-border processing of particles,a new method based on boundary symmetry mapping is designed.With taking 50 different types of benchmark functions as optimization examples,the experimental results based on the Wilcoxon-Signed rank test show that the proposed algorithm is obviously superior to the common PSO,differential evolution,attificial bee colony algorithm and quantum-behaved particle swarm optimization algorithm.关键词
粒子群优化/Beta分布函数/逆不完全伽马函数/数值优化/算法设计Key words
particle swarm optimization/Beta distribution function/inverse incomplete gamma function/numerical optimization/algorithm design分类
信息技术与安全科学引用本文复制引用
李建平,宫耀华,卢爱平,李盼池..改进的粒子群算法及在数值函数优化中应用[J].重庆大学学报,2017,40(5):95-103,9.基金项目
中国石油科技创新基金资助项目(2016D-5007-0302).Supported by PetroChina Innovation Foundation (2016D-5007-0302). （2016D-5007-0302）
