UAV Collaborative Communication Coverage Optimization Based on BBOHMM Algorithm

Unmanned aerial vehicles (UAVs) can serve as air relays to improve communication coverage by optimizing positions. UAV position optimisation is a typical nonlinear, nonconvex problem, and obtaining an analytical solution is difficult. Although swarm intelligence algorithms can effectively deal with such complex problems, they often face the challenges of complex parameter adjustment and a difficult balance between exploration and exploitation. Therefore, this paper proposes a biogeography-based optimization algorithm based on hybrid migration and mutation (BBOHMM). The algorithm dynamically combines migration rates with fitness and iteration times to enhance exploration of high-quality solutions. Meanwhile, the function of Gaussian and uniform mutation is coordinated to accelerate the solution space search in the early stage, while in the later stage, the fine exploitation is carried out around the potential optimal solutions, thereby balancing convergence speed with solution accuracy. Compared with the existing algorithms, the experimental results show that the BBOHMM can deploy UAVs more effectively and achieve a wider coverage. The test in a complex environment further verifies that the algorithm has good stability and real-time performance.