Particle swarm optimizer is a novel algorithm where a population of candidate problem solution vectors evolves "social" norms by being influenced by their topological neighbors. The standard particle swarm optimizer (PSO) may prematurely converge on suboptimal solutions that are not even guaranteed to be local extrema. A new particle swarm optimizer, called stochastic PSO (SPSO), which combined with tabu technique is presented based on the analysis of the standard PSO. And because of its local search capability, the SPSO is more efficient. And the global convergence analysis is made using the F. Solis and R. Wets' research results. Finally, several examples are simulated to show that SPSO is more efficient than the standard PSO.