免疫接种粒子群的聚类算法

将粒子群优化算法和K均值算法结合进行聚类分析,同时引入了免疫系统中的免疫接种和免疫选择机制来指导粒子的迭代过程,提出了一种基于免疫接种粒子群的聚类算法,在粒子群迭代的过程中加入免疫接种机制指导粒子的飞行方向,再通过免疫选择机制对接种的结果进行选择,确保粒子种群向更优的方向移动。实验结果证明,基于免疫接种粒子群的聚类算法基本克服了K均值算法容易受初始聚类中心影响的缺点,聚类结果稳定,而且比基于粒子群优化的聚类算法取得了更好的聚类效果。

Clustering with Immunity-Vaccination Based on Particle Swarm Optimization Algorithm

This paper proposes a clustering algorithm based on Particle Swarm Optimization Algorithm with Immunity-Vaccination (IV-PSO-KMEANS). It combines Particle Swarm Optimization (PSO) algorithm and K-means for clustering. Synchronously, Immunity-vaccination and immunity-selection mechanisms of immune system are introduced into the iterative procedure. mmunity-vaccination is used to direct the procedure of particle swarm and immunity-selection is applied to select from the results of vaccination. In result, the swarm is made to move towards a better direction. The experiments show that the IV-PSO-KMEANS algorithm overcomes the problem of K-means algorithm that the results are related to the initial clustering centers, and the results of clustering are steadier and better than algorithms based on PSO.