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Particle swarm optimization (PSO) has gained growing popularity in the recent years and is finding a wide range of important applications. Like other population based, stochastic meta-heuristics, PSO has a few algorithm parameters that need to be carefully set to achieve best execution results. This paper develops an automatic parameter tuning technique for enhancing its performance. The effectiveness of the proposed method is demonstrated on mathematical benchmark functions as well as on a real world application problem.