Optimization of Fuzzy Clustering Criteria by a Hybrid PSO and Fuzzy C-means Clustring Algorithm

This paper presents an efficient hybrid method, called fuzzy particle swarm optimization (FPSO), based on the particle swarm optimization (PSO) and fuzzy c-means (FCM) algorithms in order to solve the fuzzy clustering problem, especially for large sizes. When the problem becomes larger, the FCM algorithm may result uneven distribution of data and it is so hard to find an optimal solution in reasonable time. We use the PSO algorithm to find a good or near-optimal solution in reasonable time. To further improve the performance the PSO clustering algorithm, this paper considers seeding of the initial swarm with the result of the c-means algorithm. Various clustering simulations are experimentally compared with the FCM algorithm in order to show the efficiency and ability of the proposed algorithms