This paper presents a Bacterial particleswarm optimization (BPSO) strategy based on Particleswarm optimization (PSO) and Bacterial foraging algorithm (BFA). The velocity updating and position updating rules of particles are reinforced by two bacterial behaviors, i.e. reproduction and elimination-dispersal. Reproduction is applied to speed up the convergence rate,and elimination-dispersal provides diversity to overcomepremature problem and escape from local optima. Varioussimulation results have demonstrated that the performanceof BPSO is desirable in comparison with PSO and BFA.