Abstract: An improved differential evolution algorithm was proposed for solving the online path planning problem of unmanned aerial vehicle (UAV) low-altitude penetration in partially known hostile environments. The algorithm adopts von Neumann topology and improves its structure to maintain the diversity of the population, prevent the population from falling into local optima in the early evolution and speed up the convergence rate in the later evolution as well. The mutation operator of differential evolution is improved to speed up the convergence rate of the algorithm, so that the optimal solution of the multi-objective optimization problem can be found quickly; the coding method combined the absolute Cartesian coordinates with the relative polar coordinates is used to improve the searching efficiency. The simulation experiment of online path planning for UAV low-altitude penetration shows that the proposed algorithm has a better performance than the unimproved differential evolution algorithm.