An Efficient Hybrid Particle Swarm Optimization for Data Clustering

This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (FPSO) and fuzzyc-means (FCM) algorithms, to solve the fuzzy clustering problem, especially for large sizes. When theproblem becomes large, the FCM algorithm may result in uneven distribution of data, making it difficult tofind an optimal solution in reasonable amount of time. The PSO algorithm does find a good or nearoptimalsolution in reasonable time, but its performance was improved by seeding the initial swarm withthe result of the c-means algorithm. The fuzzy c-means, PSO and FPSO algorithms are compared using theperformance factors of object function value (OFV) and CPU execution time. It was ascertained that thecomputational times for the FPSO method outperforms the FCM and PSO method and had higher solutionquality in terms of the objective function value (OFV).