关键词: 粒子群算法, K均值聚类算法, 疫苗接种, 免疫选择

Abstract: After analyzing the disadvantages of initialization sensitive and local extremum of the K-means algorithm, this paper proposes a hybrid clustering algorithm based on disturbance immune particle swarm optimization and K-means. The new clustering algorithm uses K-means to divide the particles into several categories and then chooses the optimal clustering domain to produce vaccine. After that, it adopts the vaccination and immune selection to improve the diversity of the particles. Meanwhile, in the algorithm, the disturbed arithmetic operators is introduced to break away from the local extremum by changing the movement of the particles when the times of the continuous stagnation exceed the threshold. The K-means clustering algorithm is employed to improve the convergence precision of the algorithm when the times of the disturbance meets the maximum. The experimental results show that the convergence accuracy and stability of the algorithm are good.

Key words: particle swarm optimization algorithm, K-means clustering algorithm, vaccination, immune selection