A comparative study of meta-heuristic algorithms for solving UAV path planning

Path planning of unmanned aerial vehicle (UAV) is an important preliminary step in UAV flight mission which can be fulfilled by finding the optimum solution for an optimization problem. As a complicated NP-hard search problem, it is necessary to successfully avoid the obstacles while optimizing the flight route according to linear and non-linear constraints. The global search capability of nature-inspired algorithms has made them an attractive choice to address the complexity of UAVs path planning problem. In this work, the UAV path planning problem is modeled as a single objective optimization problem in a static two-dimensional space, where the path is constrained to avoid obstacles. This paper compares the performance of several well-known nature-inspired algorithms on the UAV path planning problem from the earliest to the newest one and shows the interest of DE and TLBO for this path planning problem.