基于改进差分进化算法的无人机在线低空突防航迹规划

为了解决无人机在部分未知敌对环境中的低空突防航迹规划问题,提出了一种改进的差分进化算法.该算法的进化模型采用冯.诺伊曼拓扑结构,并对其进行拓展,使种群在进化初期保持多样性,避免进化早期陷入局部最优,而进化后期加快收敛速度.该算法改进了差分进化算子中的变异操作,从而加快算法的收敛速度,快速找到多目标优化问题的最优解;同时,采用将绝对笛卡儿坐标和相对极坐标相结合的编码方式以提高搜索效率.将该算法用于无人机在线航迹规划仿真实验,并和未改进的算法结果作比较,验证了该算法的有效性.

Online path planning for UAV low-altitude penetration based on an improved differential evolution algorithm

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.