Skip to Main Content
A new clustering algorithm is proposed based on particle swarm optimization (PSO). The main idea of the new algorithm is to solve clustering problem using the fast search ability of the particle swarm optimization, each particle is composed of a cluster center vector, and represents a possible solution of the clustering problem. To escape from local optimum, a new idea is proposed, that is the neighborhood structure of individual optimum is enriched using the probabilistic jumping property of the simulated annealing (SA). The individual optimum of the particles is disturbed randomly, that is the data pattern clustering label is changed randomly, so the search ability of the global space is enhanced. The experimental results on different datasets show that the new algorithm has better performance than particle swarm optimization and K-means algorithm, has better global convergence, and it is an effective clustering algorithm.