计算机工程与科学2011，Vol.33Issue(5)：97-101,5.DOI:10.3969/j.issn.1007-130X.2011.05.019
一种基于均值的云自适应粒子群算法
A Cloud Adaptive Particle Swarm Optimization Algorithm Based on Mean
摘要
Abstract
Based on the cloud adaptive theory, the particle swarm optimization algorithm is improved and the particle swarm is divided into three populations. It modifies the inertia weight using a cloud method, and meanwhile modifies the “social” and “cognitive” sections, and introduces the notion of mean, and proposes an improved cloud adaptive theory particle swarm optimization algorithm named MCAPSO. The greatest advantage of the method is that the algorithm in the later iteration, when the different value between an individual optimal to some particle corresponding of the fitness value and a global optimal corresponding to the fitness value is significant, overcomes the shortcoming that the algorithm does not benefit from converges to the optimal solution. Numerical experience shows that,MCAPSO runs less iteration to find the optimal solution, and the average time is lower. The average time cost is reduced accordingly.关键词
粒子群优化/均值/云理论/自适应惯性权重调整Key words
particle swarm optimization(PSO) / mean / cloud theory / adaptive inertia weight adjusting分类
信息技术与安全科学引用本文复制引用
刘洪霞,周永权..一种基于均值的云自适应粒子群算法[J].计算机工程与科学,2011,33(5):97-101,5.基金项目
国家自然科学基金资助项目(60461001) （60461001）
广西自然科学基金资助项目(0542048,0832082) （0542048,0832082）
