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.