计算机工程与应用2016，Vol.52Issue(12)：19-25,48,8.DOI:10.3778/j.issn.1002-8331.1510-0254
基于PAM和均匀设计的并行粒子群优化算法
Parallel PSO algorithm based on PAM and uniform design
摘要
Abstract
Clustering technique is an important method in data mining. PAM(Partitioning Around Medoids)is one of clustering algorithms based on partitioning methods. It attempts to divide n data objects into k partitions. In the parallel PSO(Particle Swarm Optimization)algorithms, it needs to divide the swarm into several sub-swarms non-overlapping with each other. Therefore, PAM is introduced to divide the swarm. Clustering makes sure particles within the same sub-swarm are relatively concentrative, so that they can be easier to learn each other. This makes the limited time be spent on the most effectively searching, so as to improve the searching efficiency of an algorithm. In order to evenly explore the whole solution spaces, uniform design is introduced to generate an initial population. This is to ensure that the population members are scattered uniformly over the feasible solution space. In evolution, uniform design is also introduced to replace some worse individuals. This paper presents a parallel PSO algorithm employing PAM and uniform design. It combines and takes full advantage of the merits of the two. The experimental results performed on several test problems demonstrate that the proposed algorithm has higher performance and convergence accuracy than traditional parallel PSO.关键词
并行/围绕中心点的划分(PAM)/均匀设计/粒子群优化Key words
parallel/Partitioning Around Medoids(PAM)/uniform design/Particle Swarm Optimization(PSO)分类
信息技术与安全科学引用本文复制引用
封俊红,张捷,朱晓姝..基于PAM和均匀设计的并行粒子群优化算法[J].计算机工程与应用,2016,52(12):19-25,48,8.基金项目
玉林师范学院博士科研启动基金（No.G2014005）。 （）
