Abstract: The Quantum-behavior Particle Swarm Optimization (QPSO) potential well center is restricted to the super rectangle which is made up of the local optimal position and the global optimal position. The information sharing mechanism among particles is simple, and the algorithm has the problems of premature convergence and low optimization efficiency. To solve this problem, an improved QPSO algorithm, namely the QPSO algorithm (LSL-QPSO) which integrates social learning and Lévy flights. Firstly, the social learning strategy is used to update the non optimal particle and improve the global search ability. Then, the Lévy flights strategy is introduced to overcome the shortcoming of the low efficiency of the optimal particle in the social learning mechanism, and further improve the convergence accuracy and search efficiency of the algorithm. Finally, four typical Benchmark functions are tested. The results show that the convergence accuracy, search efficiency and universality of the LSL-QPSO algorithm are ahead of QPSO and other similar QPSO improved algorithms.