针对不确定性环境下的多任务区遍历侦察决策问题, 将整个任务执行过程分为两个阶段, 首先根据侦察任务区的信息及UAV自身性能, 采用离散布谷鸟搜索算法解决侦察路径最优化问题, 使遍历侦察全部任务区的航路最短。然后根据任务载荷及待侦察任务区的特性, 在确保遍历侦察全部任务区及满足最小侦察收益的前提下, 利用改进的布谷鸟搜索算法为每个待侦察任务区分配最优的任务侦察时间, 从而使整个侦察任务过程的信息收益最大化。最后通过仿真验证了决策方案的有效性和可行性, 通过与传统遗传算法的对比分析, 证明改进的布谷鸟搜索算法对此类侦察决策问题的运行效率与传统遗传算法相比有较大提高, 从而为UAV多任务区的最优化遍历侦察问题提供了科学的决策依据。

The multi-task area reconnaissance decision-making problem in an uncertain environment is considered, and the entire task-performing process is divided into two stages.According to the information of reconnaissance task areas and the performance of the UAV itself, a discrete Cuckoo Search Algorithm (CSA) is adopted to solve the reconnaissance route optimizing problem, so that the reconnaissance path of the entire task area is the shortest.Then, according to the characteristics of the task load and the reconnaissance task area, and under the premise of ensuring the reconnaissance of the entire task area and the minimum reconnaissance gains, the improved CSA is used to allocate the optimal task reconnaissance time for each reconnaissance task area, so that the information gains in the entire reconnaissance process is the maximum.Simulation results have proved the effectiveness and feasibility of the proposed algorithm.Compared with classic genetic algorithms, the improved CSA has a higher operating efficiency on such reconnaissance decision-making problems.It provides a reference for the scientific decision-making of UAV multi-task area reconnaissance optimizing problem.