基于K-Means变异算子的混合遗传算法聚类研究  

Hybrid genetic algorithm clustering analysis based on K-Means mutation operator

作　　者：耿跃[1] 任军号[1] 吉沛琦[1] 

机构地区：[1]西北工业大学自动化学院,西安710072

出　　处：《计算机工程与应用》2011年第29期151-153,221,共4页Computer Engineering and Applications

基　　金：西咸一体化战略系统研究(No.2010KRM18)

摘　　要：遗传算法具有良好的全局搜索能力,但有过早收敛和过慢结束的缺点。K-Means算法具有很强的局部搜索能力,但算法有对初始聚类中心敏感而易陷入局部最优解。针对上述问题,提出了基于K-Means变异算子的混合遗传算法聚类,将K-Means算法的局部搜索能力与遗传算法的全局寻优搜索能力相结合,在遗传算法中引入K-Means变异算子,采用符号编码、自适应变异、最优个体保留策略的混合遗传算法。仿真实验表明,该算法有效克服了遗传算法过慢收敛和K-Means算法陷入局部收敛的问题,从而得到更好的聚类效果。Genetic algorithm has better global search capability,but has the shortcomings of premature convergence and slow end.K-Means algorithm has strong local search ability,but it＇s sensitive to the initial cluster centers and easy to get stuck at locally optimal value.To solve such problems, it presents a hybrid genetic algorithm based on K-Means mutation operator.It combines the locally searching capability of the K-Means algorithm with the global optimization capability of genetic algorkhm, and introduces the K-Means mutation operator into the genetic algorithm.It＇s a hybrid algorithm using symbolic coding, adaptive mutation, and optimal individual retention policies.Simulation results show that the algorithm has effectively overcome the slow convergence of genetic algorithm and the locality convergence of K-Means algorithm, in order to get better clustering.

关 键 词：聚类分析 K-MEANS算法 K-Means变异算子 遗传算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]