杨晓庆, 左为恒, 李昌春. 基于K-Means变异算子的混合PSO算法聚类研究[J]. 微电子学与计算机, 2011, 28(7): 57-60.
引用本文: 杨晓庆, 左为恒, 李昌春. 基于K-Means变异算子的混合PSO算法聚类研究[J]. 微电子学与计算机, 2011, 28(7): 57-60.
YANG Xiao-qing, ZUO Wei-heng, LI Chang-chun. Hybrid PSO Algorithm Clustering Analysis Based on K-Means Mutation Operator[J]. Microelectronics & Computer, 2011, 28(7): 57-60.
Citation: YANG Xiao-qing, ZUO Wei-heng, LI Chang-chun. Hybrid PSO Algorithm Clustering Analysis Based on K-Means Mutation Operator[J]. Microelectronics & Computer, 2011, 28(7): 57-60.

基于K-Means变异算子的混合PSO算法聚类研究

Hybrid PSO Algorithm Clustering Analysis Based on K-Means Mutation Operator

  • 摘要: 提出了基于K-Means算子的混合粒子群优化算法聚类,将K-Means算法的局部搜索能力与粒子群优化算法的全局寻优搜索能力相结合,根据群体适应度变化的情况自适应调整权重,并对种群中性能较差的粒子进行交叉选择,能充分挖掘群体本身信息,又能不断引入附加信息.数据集仿真实验表明,该算法有效的克服了传统粒子群优化算法过慢收敛和K-Means算法陷入局部收敛的问题,从而得到更好的聚类效果.

     

    Abstract: This paper presents a hybrid PSO algorithm based on K-Means operator.It combines the locally searching capability of the K-Means algorithm with the global optimization capability of genetic algorithm, and introduces the K-Means operator into the PSO algorithm.It's a hybrid algorithm using symbolic coding, adaptive mutation, and optimal individual retention policies.Simulation results show that the algorithm has effectively overcomes the slow convergence of PSO algorithm and the locality convergence of K-Means algorithm, in order to can get better clustering.

     

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