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

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