精英扩散蚁群优化算法求解运输无人机三维路径规划

针对受灾山区运输物资的三维无人机路径规划问题，提出了一种精英扩散蚁群优化算法EDACO，首先通过极值限定策略限定了信息素浓度的范围,防止算法前期陷入局部最优;然后采用精英策略改进信息素浓度更新公式，加强优质个体对种群的影响力; 再引入信息素扩散策略，加强距离较近个体间的交流协作，以防止蚂蚁个体间联系不紧密造成的算法停滞。最后，将精英扩散蚁群优化算法、传统蚁群算法、遗传算法和萤火虫算法运用于4个山区受灾无人机运输实例中，结果表明了EDACO的优越性和有效性，且该算法对无人机三维路径规划问题有着良好的适应性。

An elite diffusion ant colony optimization algorithm for solving 3D path planning of transportation UAV

Aiming at the problem of 3D drone path planning for transportation materials in the affected mountainous areas, an elite ant colony algorithm is proposed. Firstly, the algorithm limits the range of pheromone concentration through an extreme value limiting strategy to prevent the algorithm from falling into a local optimum in the early stage. Secondly, the elite strategy is used to improve the pheromone concentration update formula and strengthen the influence of high-quality individuals on the population. Pheromone diffusion strategies are introduced to strengthen communication and collaboration between individuals closer to each other, in order to prevent the stagnation of the algorithm caused by insufficiently close ant individuals. Finally, the elite diffusion ant colony algorithm, traditional ant colony algorithm, genetic algorithm and firefly algorithm are applied to four examples of disaster-resistant drone transportation in mountainous areas, proving the superiority and effectiveness of the improved algorithm. The algorithm has a good adaptability to the problem of 3D path planning for UAV.