融合粒子群和混合蛙跳的模糊C-均值算法

Improved FCM algorithm based on PSO and SFLA

针对模糊聚类算法中存在的对初始值敏感、易陷入局部最优等问题,提出了一种融合粒子群算法和混合蛙跳算法的模糊C-均值聚算法。通过设计了一种新颖的搜索粒度系数,充分利用粒子群算法收敛速度快、局部搜索能力强的优点与混合蛙跳算法全局寻优能力强、跳出局部最优能力好的特点,同时对SFLA中更新算法进行了改进。实验结果表明,该算法提高了模糊聚类算法的搜索能力和聚类效果,在全局寻优能力、跳出局部最优能力、收敛速度等方面具有优势。

The traditional fuzzy clustering algorithm is sensitive to the initial point and easy to fall into local optimum.In order to overcome these flaws,an improved Fuzzy C-Mean（FCM） algorithm which combines the Particle Swarm Optimization（PSO） algorithm and Shuffled Frog Leaping Algorithm（SFLA） was proposed.Through designing a new search granularity factor,it could take advantage of the fast convergence speed,strong local search ability of PSO and strong global search capability,ability to jump of local optimum of SFLA,making the integration of PSO and SFLA better.At the same time,the update algorithm of SFLA was improved.The experimental results show that this method improves the search capability and the clustering performance of fuzzy clustering algorithm,and it has the advantages in the global search ability escaping from local optimum capacity,and convergence speed.