关键词: K-means, 并行, 粒子群, 优化

Abstract: By introducing K-means for the parallel particle swarm optimization based on an island model，the populations are divided into several sub-populations.It can not only make the location of the particles in the same sub-population be in the relative concentrative，and be relatively easy to learn，but also improve the search efficiency，so that the limited time will be spent on the most effective search.According to the characteristics of them，the parallel algorithms are improved.When certain conditions are met，it carries on communications，so that ineffective communications can be avoided to reduce the time spent for communications.The simulation results confirm that the algorithms have a high convergence speed and convergence accuracy.

Key words: K-means, parallel, particle swarm optimization, optimization