计算机工程与应用2012，Vol.48Issue(5)：29-31,3.DOI:10.3778/j.issn.1002-8331.2012.05.008
改进的云自适应粒子群算法
Modified adaptive PSO algorithm based on cloud theory
张锦华1
作者信息
- 1. 昆明工业职业技术学院电气工程系,昆明650302
- 折叠
摘要
Abstract
This paper proposes a novel Modified Adaptive Particle Swarm Optimization(MCAPSO) algorithm based on cloud theory to improve the optimum speed and performance of the PSO algorithm. The particles are divided into three groups based on the fitness of the particle in order to adopt different inertia weight generating strategy and evolutionary strategy and effective balance between the local and global search ability is achieved. This paper chooses five reference functions to have a test and compares the results with other PSO algorithms. The simulation results verify the effectiveness of this approach.关键词
粒子群算法/云自适应惯性权重/进化策略Key words
Particle Swarm Optimization (PSO)/ adaptive inertia weight based on cloud theory/ evolutionary strategy分类
信息技术与安全科学引用本文复制引用
张锦华..改进的云自适应粒子群算法[J].计算机工程与应用,2012,48(5):29-31,3.
