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

YANG Xiao-qing; ZUO Wei-heng; LI Chang-chun

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

更新时间：2026-09-04

- Hybrid PSO Algorithm Clustering Analysis Based on K-Means Mutation Operator
- Vol. 28, Issue 7, Pages: 57-60(2011)
- 作者机构：
  
  重庆大学输配电装备及系统安全与新技术国家重点实验室机构,重庆,400030
- 作者简介：
- 基金信息：
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- Published：2011
- 稿件说明：
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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]. 2011, 28(7): 57-60.
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. DOI：

YANG Xiao-qing, ZUO Wei-heng, LI Chang-chun. Hybrid PSO Algorithm Clustering Analysis Based on K-Means Mutation Operator[J]. 2011, 28(7): 57-60. DOI：

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.Its 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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