Fuzzy Clustering with Improved Swarm Optimization and Genetic Algorithm: Hybrid Approach

Fuzzy c-means clustering is one of the popularly used algorithms in various diversified areas of applications due to its ease of implementation and suitability of parameter selection, but it suffers from one major limitation like easy stuck at local optima positions. Particle swarm optimization is a globally adopted metaheuristic technique used to solve complex optimization problems. However, this technique needs a lot of fitness evaluations to get the desired optimal solution. In this paper, hybridization between the improved particle swarm optimization and genetic algorithm has been performed with fuzzy c-means algorithm for data clustering. The proposed method has been compared with some of the existing algorithms like genetic algorithm, PSO, and K-means method. Simulation result shows that the proposed method is efficient and can divulge encouraging results for finding global optimal solutions.