Particle Swarm Optimization (PSO) is easy to fall into local minimum, and has poor global search ability. Many improved algorithms cannot optimize PSO performance fully by using a single search strategy in a way. In order to solve the problem, a novel PSO with Firefly Behavior and Levy Flight (FBLFPSO) was proposed. The local search ability of PSO was improved to avoid falling into local optimum by using improved self-regulating step firefly search strategy. Then, the principle of Levy flight was taken to enhance population diversity and improve the global search ability of PSO, which contributed to escape from local optimal solution. The simulation results show that, compared with the existing correlation algorithms, the global search ability and the search accuracy of FBLFPSO are greatly improved.