关键词: 聚类, 模糊C均值, 粒子群优化算法, 混沌粒子群优化算法, 图像分割

Abstract: Fuzzy C-Means clustering algorithm（FCM） is one of the widely applied fuzzy algorithms at present, but FCM algorithm has some limitations. It is sensitive to initial clustering center and membership matrix and likely converges into the local minimum, so it can not get the best clustering results. Combining the Particle Swarm Optimization（PSO） and FCM, a new FCM clustering algorithm based on chaos particle swarm optimization（CPSO） is proposed. The algorithm can make use of the global optimization of PSO to jump out of local minimum, and can get a better clustering effect. In order to avoid stagnation of particles in the iteration, the algorithm introduces the chaotic variables, generates a chaos sequence based on the current global best position, and replaces randomly a particle of the particle swarm with the particle that has optimal-adaptive value in the chaos sequence. The algorithm is applied in image segmentation. The experimental results show that the new algorithm can segment the image effectively and properly, and has the good robustness to noises and good adaptability.

Key words: clustering, Fuzzy C-Means（FCM）, Particle Swarm Optimization（PSO）, Choas Particle Swarm Optimization（CPSO）, image segmentation