基于蝙蝠算法的多无人机协同侦察任务规划

Multi-UAV cooperative reconnaissance mission planning based on bat algorithm

多无人机协同侦察规划问题是指多架无人机对多目标进行侦察,以最小化无人机数量和总行驶路径最短为目标构建数学模型进行求解的问题。蝙蝠算法是新兴的群体智能算法,前景广阔,在多领域应用具有显著的效果。针对多无人机协同侦察规划问题,对场景进行编码,利用蝙蝠算法进行求解,并将所得结果与遗传算法和粒子群算法求得的结果进行对比。实验结果表明,在求解多无人机协同侦察任务规划问题时,蝙蝠算法相比遗传算法、粒子群算法,具有更好的稳定性和更快的处理速度。

Multi-UAV cooperative reconnaissance planning problem refers to the problem that multiple UAVs reconnaissance multi-targets to solve the problem of minimizing the number of UAVs and the shortest total driving path.The bat algorithm is an emerging swarm intelligence algorithm with broad prospects and significant effects in many fields.In this paper,for the multi-UAV cooperative reconnaissance planning problem,the scene is coded,and the bat algorithm is used to solve the problem,and the obtained results are compared with the results obtained by genetic algorithm and particle swarm algorithm.The experimental results show that the bat algorithm has better stability and faster processing speed than the genetic algorithm and particle swarm optimization algorithm when solving the multi-UAV cooperative reconnaissance mission planning problem.