关键词: 混合蛙跳算法, 粒子群算法, 模糊C-均值, 目标函数

Abstract: 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.

Key words: Shuffled Frog Leaping Algorithm (SFLA), Particle Swarm Optimization (PSO) algorithm, Fuzzy C-Mean (FCM), objective function