An Improved Qpso Algorithm Base On Social Learning And Levy Flights

The centre of the potential well of the quantum-behaviour particle swarm optimization (QPSO) 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 single, and the algorithm has the problems of premature convergence and low optimization efficiency. To solve this problem, an improved QPSO algorithm is proposed, which integrates social learning and Levy flights (LSL-QPSO). Firstly, the social learning strategy is used to update the non-optimal particle and improve the global search ability. Then, the Levy 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.