计算机工程与应用2011，Vol.47Issue(20)：41-43,3.DOI:10.3778/j.issn.1002-8331.2011.20.012
模糊系统的微粒群并行聚类算法
Parallel cluster algorithm based on fuzzy systemic particle swarm optimization
摘要
Abstract
To solve large numbers of computations in the problem of large-scale data clustering, a particle swarm optimization of fuzzy systems in parallel k-means clustering algorithm is proposed to deal with this problem.The method adjusts dynamically inertia weight and acceleration factor of particle swarm optimization with fuzzy rules, the problems of particle mobility loss and the end of evolution can be dealt with successfully.the algorithm maintains individual diversity and solves the premature convergence problem.Task parallelization and partially asynchronous communication of the algorithm are employed to decrease computing time.The simulation experiments indicate the algorithm helps increase computing speed and improve the clustering quality.关键词
并行聚类/模糊系统微粒群优化/任务并行/异步通信Key words
parallel clustering/fuzzy systems particle swarm optimization/task parallelism/asynchronous communication分类
信息技术与安全科学引用本文复制引用
王泽,蔡焕夫,高平安..模糊系统的微粒群并行聚类算法[J].计算机工程与应用,2011,47(20):41-43,3.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.61070232) （the National Natural Science Foundation of China under Grant No.61070232）
广东省自然科学基金(No.06024881) （No.06024881）
广东金融学院校级课题(No.09xJ02-06). （No.09xJ02-06）
