Particle Swarm Optimization Algorithm Based on Combining Global-Best Operator and Levy Flight

In order to enhance the optimization performance of the particle swarm optimization algorithm with Levy Flight (LFPSO), this paper proposes an improved LFPSO (ILFPSO), namely PSO based on combining the global-best operator and Levy Flight. First, the Levy Flight operator is accurately improved so that it can prevent the algorithm from generating invalid solutions, and an improved Levy Flight operator is obtained. Then because of the advantage of the global-best operator which has both some global and strong local search ability, this paper combines the global-best operator and improved Levy Flight in order to balance the global and local optimization ability of the algorithm. Finally, the velocity boundary is updated dynamically, which is helpful to find the global optimal solution in the early search stage and local optimal solutions in the later stage. 28 benchmark functions are used to evaluate the feasibility of ILFPSO. The experimental results show that, compared with 4 state-of-the-art PSO variants, such as LFPSO, ELPSO, SRPSO and RLPSO, ILFPSO obtains stronger competitive power, better universality and faster running speed.