Multiple Unmanned Aerial Vehicles Path Planning Based on Collaborative Differential Evolution

In this paper, we propose a collaborative differential evolution algorithm (CODE) to solve the problem of the multiple unmanned aerial vehicles (multi-UAVs) path planning in the three-dimensional (3D) environment. Because the centralized differential evolution algorithm (DE) solves the multi-UAVs path planning problem, the dimension is too high and the amount of computation is too large, CODE divides the entire population of DE into several groups equally, and each group searches in parallel, calculates the individual cost of each UAVs and establishes an information exchange mechanism to calculate the cooperation cost between UAVs, and outputs the path corresponding to a UAV. Experimental results show that the proposed algorithm is significantly better than other comparative algorithms.