Abstract
Among the nature inspired heuristic or meta-heuristic optimization algorithms Bee Colony Optimization (BCO) and Particle Swarm Optimization(PSO) algorithms are widely used to solve clustering problems. In this paper, two hybridized optimization algorithms PS-BCO-K and K-PS-BCO are proposed based on PSO, BCO and K-means for data clustering. To validate the proposed algorithms, five standard data sets are considered. The result shows that, the proposed methods give better quality solution as compared to some existing well known heuristic or meta-heuristic optimization algorithms. The simulation results infer that the proposed algorithms can be efficiently used for real time data clustering problem.
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