计算机技术与发展Issue(2)：49-51,56,4.DOI:10.3969/j.jssn.1673-2013.02.012
基于函数最优解问题的粒子群算法改进
Partical Swarm Optimization Improvement Based on Optimal Solution to Functions
摘要
Abstract
A depth-research found that partical swarm algorithm is easy to fall into local minima,iterative post-slow rate of convergence, low accuracy and so on,then many scholars have made improvements and successfully applied to a variety of practical problems. In order to improve the performance of partical swarm optimization,it works over the particle swarm optimization and the proved methods in order to solve the function of the optimal solution fastly and accurately. And it combines the time-varying weights with the constriction factor to improve the partical swarm optimization. Then use the method to solve the optimal solution of functions. Experiments show that the method has the advantages with the band time-varying weights or with a compression factor algorithm,at the same time makes the con-vergence faster,improves the accuracy of the optimal solution of the function,and improve the performance by adjusting parameters.关键词
粒子群算法/压缩因子/时变权重Key words
partical swarm optimization algorithm/conpression factor/time-varying weights分类
信息技术与安全科学引用本文复制引用
王莉荣,祁云嵩..基于函数最优解问题的粒子群算法改进[J].计算机技术与发展,2013,(2):49-51,56,4.基金项目
国家自然科学基金资助项目(61100116) （61100116）
