计算机工程与应用2016，Vol.52Issue(17)：49-53,5.DOI:10.3778/j.issn.1002-8331.1512-0149
基于相似度的改进粒子群优化算法
Improved particle swarm optimization algorithm based on similarity
摘要
Abstract
The biggest flaw of PSO(Particle Swarm Optimization)is easy to premature convergence, although some improved PSO algorithms can increase the ability of convergence, but they can not fundamentally solve the problem of premature convergence. In this paper, a new concept of similarity between particles is proposed to measure the degree of the diversity of particle swarm, and for the purpose of gaining a gradual progress of global optimal solution, adaptive thresholds are used to control the adjustment of convergence rate of particle swarm algorithm. In each iteration stage, Gaussian noise and other disturbances based on the similarity are also used to readjust the position of the particle in order to avoid the particle plunging local optimum. Experimental results and theoretical discussion show that new algorithm can effectively improve the globally searching ability of PSO, and effectively avoid the premature convergence.关键词
粒子群优化/相似度/阈值控制/高斯噪声扰动Key words
Particle Swarm Optimization(PSO)/similarity/threshold control/disturbance of Gaussian noise分类
信息技术与安全科学引用本文复制引用
杨杰,万仁霞,刘楷..基于相似度的改进粒子群优化算法[J].计算机工程与应用,2016,52(17):49-53,5.基金项目
国家自然科学基金（No.61163017，No.61440044）；宁夏自然科学基金（No.NZ15094）。 （）
