计算机与数字工程2017，Vol.45Issue(7)：1252-1255,1293,5.DOI:10.3969/j.issn.1672-9722.2017.07.003
粒子群算法的改进及其在优化函数中的应用
Improvement of Particle Swarm Algorithm and Its Application in Optimization Function
摘要
Abstract
The defection of particle swarm optimization algorithm means that an increase in iterations decreases swarm diversity and causes prematurity,thus probably producing local optimization results.However,immune mechanism of biology is capable of effectively overcoming these shortcomings.Firstly particle swarm algorithm is organically combined with immune principle to form immune particle swarm optimization algorithm (IMPSO),then certain improvements will be made in inertia coefficient and learning factor of PSO algorithm and finally effect of algorithm improvement will be verified through calculation of typical optimization function.关键词
粒子群优化算法/免疫原理/免疫粒子群优化算法/惯性系数/学习因子Key words
particle swarm optimization algorithm/immune theory/immune particle swarm optimization algorithm/inertia coefficient/learning factor分类
信息技术与安全科学引用本文复制引用
马发民,张林,王锦彪..粒子群算法的改进及其在优化函数中的应用[J].计算机与数字工程,2017,45(7):1252-1255,1293,5.基金项目
国家自然科学基金项目(编号:60472121) （编号:60472121）
商洛学院自然科学研究项目(编号:15SKY007)资助. （编号:15SKY007）
