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

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.

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.