A Novel Leader-Follower-Based Hybrid Particle Swarm-Grey Wolf Optimization Algorithm for Path Planning of Unmanned Aerial Vehicle

This paper presents a novel hybrid Particle Swarm-Grey Wolf Optimization algorithm with leaderfollower structure (called LFPS-GWO) is proposed to solve the constrained unmanned aerial vehicle (UAV) path planning problem. In LFPS-GWO, the particles are defined as the leaders responsible for global exploration and the grey wolves are employed as the followers to explore the optimal solutions around the leaders. In addition, the Lévy flight and dynamic weight adjustment strategy are implemented to enhance the exploration capability and convergence speed. Furthermore, the LFPS-GWO is applied to solve the UAV path planning problem. Experimental results show that the performance of LFPS-GWO is significantly better than other compared algorithms in eight CEC2017 benchmark functions and UAV path planning problems.