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Adaptive inertia weight is proposed to rationally balance the global exploration and local exploitation abilities for particle swarm optimization. This paper describes an adaptive strategy for tuning the inertia weight parameter of the PSO algorithm - Exponential type adaptive inertia weighted Particle Swarm Optimization (EPSO). This adaptive tuning strategy is based on the inertia weight dynamic decreased according to iterative generation increasing. The stochastic convergence of the EPSO has been analyzed with the probability density functions of objective function. EPSO algorithm is tested with a set of 5 benchmark functions and compared with standard PSO. Experimental results indicate that the EPSO algorithm improves the search performance on the benchmark functions significantly.