Particle Swarm Optimization and Differential Evolution in Fuzzy Clustering

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Abstract

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means (FCM) is one of the most popular clustering methods based on minimization of a criterion function because it works fast in most situations. However, it is sensitive to initialization and is easily trapped in local optima. Particle swarm optimization (PSO) and differential evolution (DE) are two promising algorithms for numerical optimization. Two hybrid data clustering algorithms based the two evolution algorithms and the FCM algorithm, called HPSOFCM and HDEFCM respectively, are proposed in this research. The hybrid clustering algorithms make full use of the merits of the evolutionary algorithms and the FCM algorithm. The performances of the HPSOFCM algorithm and the HDEFCM algorithm are compared with those of the FCM algorithm on six data sets. Experimental results indicate the HPSOFCM algorithm and the HDEFCM algorithm can help the FCM algorithm escape from local optima.