分布式无人机网络覆盖优化算法

针对非均匀目标区域中的热点区域覆盖优化场景，提出一种分布式无人机网络覆盖优化算法。首先对满足网络连通性的最少无人机节点数目和热点区域覆盖范围进行估计，其次融入热点区域信息改进布谷鸟算法位置更新方程并重构优化目标函数，然后对发现概率参数进行自适应调整，最终实现热点区域覆盖率的重点优化。在仿真实验分析中，在相同仿真环境下与标准布谷鸟算法和其他经典算法进行对比，结果表明所提算法的热点区域覆盖率较其他算法提升了约4%，迭代次数减少了约30次，证明了该算法收敛速度快、耗时少，能够更加有效地提高热点区域的覆盖率。

Research on Distributed UAV Network Coverage Optimization Algorithm

A distributed UAV network coverage optimization algorithm is proposed for the hotspot coverage coverage optimization scenario in the non uniform target area. Firstly, the number of minimum UAV nodes that satisfy the network connectivity and the coverage of the hotspot area are estimated. Secondly, the hotspot information is added to improve the location update equation of the cuckoo algorithm and the optimization objective function is reconstructed. Then the adaptive probability parameters are adaptively adjusted. Finally, the key optimization of hotspot area coverage is achieved. In the simulation experiment analysis, compared with the standard cuckoo algorithm and other classical algorithms in the same simulation environment, the results show that the coverage of the hotspot area of the proposed algorithm is improved by about 4% compared with other algorithms, and the number of iterations is reduced by about 30 times. It is proved that the algorithm has fast convergence speed and less time, which can improve the coverage of hotspots more effectively.