UAV path planning using GSO-DE algorithm

Path planning is an optimal problem how to plan optimal flight path of unmanned aerial vehicle (UAV) in the complex environment of war. A hybrid group search optimizer (GSO) with differential evolution (DE) is proposed to solve UAV path planning problem. Firstly, GSO is applied to update flight path of UAV by the search angle and the distance. DE is used to modify the feasible path of UAV within the search area by means of self-organization and self-regulatory in the evolutionary process. Then, UAV can find the safe path by connecting the chosen points of the coordinates while avoiding the threats area and costing minimum fuel. This approach can accelerate the global convergence speed. Finally, experimental results demonstrate that the proposed GSO-DE algorithm is effective and feasible in UAV path planning.