火力与指挥控制2016，Vol.41Issue(5)：56-61,6.
云模型改进惯性权重的混沌交替粒子群算法
Chaos Alternation Particle Swarm Optimization Algorithm Improved Inertia Weight Based on Cloud Model
摘要
Abstract
The optimization performance of the standard particle swarm optimization algorithm is adjusted by reducing the inertia weightlinear,which lack of intelligent mechanism and easy to bring into the prematurity and local stalemate in the evening of the algorithm. A chaos alternation particle swarm optimization algorithm improved the inertia weight based on cloud model is proposed to solve these problems. The inertia weight ω of the particle swarm optimization algorithm is adjusted by cloud model intelligently according to the iterative transformation of the particles,and the whole and local searching capabilities of the particle swarm optimization algorithm get balanced,and prevent it into the local stalemate. In addition,determine the stability of the particles,and do chaos disturbance to the stable particles which maybe bring into the local stalemate,make it jump out form the local stalemate and close to the optimization position further. It shows from the experiment and analysis that the chaos alternation particle swarm optimization algorithm improved the inertia weight based on cloud model can be able to jump out from the local stalemate with a higher optimization precision,and the average iterative numbers are reduced by 13.73 %~20.11 % than other researches when the algorithm gets absolutely convergence.关键词
粒子群/云模型/惯性权重/稳定粒子/混沌交替Key words
particle swarm optimization/cloud model/inertia weight/stable particles/chaos alternation分类
信息技术与安全科学引用本文复制引用
李纪真,孟相如,崔文岩,杨婷..云模型改进惯性权重的混沌交替粒子群算法[J].火力与指挥控制,2016,41(5):56-61,6.基金项目
国家自然科学基金资助项目 （）
