计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (16): 130-131.DOI: 10.3778/j.issn.1002-8331.2010.16.038
• 数据库、信号与信息处理 • 上一篇 下一篇
刘 琼，罗 可
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LIU Qiong，LUO Ke
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摘要： 针对基本粒子群算法的早熟收敛和收敛较慢的问题，提出了一种带变异操作的粒子群聚类算法。算法中对出现早熟收敛的种群采取变异操作，使其能够跳出局部最优解。对Iris植物样本数据的测试结果表明：该算法具有很好的全局收敛性和较快的收敛速度。
关键词: 粒子群算法, 聚类分析, K均值算法
Abstract: Aiming at premature convergence and convergence speed of basic particle swarm optimization algorithm，the clustering algorithm based on particle swarm optimization algorithm with mutation is proposed.A mutation operator is used in the algorithm for some particles to escape from the local optimal solution.The algorithm is evaluated on Iris plants database.Results show that the algorithm not only avoids local optima，but also increases the convergence speed.
Key words: Particle Swarm Optimization algorithm, clustering analysis, K-means algorithm
中图分类号:
TP301
刘 琼，罗 可. 一种带变异操作的粒子群聚类算法[J]. 计算机工程与应用, 2010, 46(16): 130-131.
LIU Qiong，LUO Ke. Clustering algorithm based on Particle Swarm Optimization with mutation[J]. Computer Engineering and Applications, 2010, 46(16): 130-131.
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链接本文: http://cea.ceaj.org/CN/10.3778/j.issn.1002-8331.2010.16.038
http://cea.ceaj.org/CN/Y2010/V46/I16/130
