Research on UAV self-organizing network coverage optimization based on an improved Hippo algorithm
This paper focuses on the coverage deployment optimization of self-organizing UAV networks in three-dimensional space and proposes an improved Hippopotamus Optimization Algorithm. Latin hypercube initialization, an adaptive convergence factor, and a tangential flight strategy are introduced to improve the uniformity of the initial population, enhance the balance between global exploration and local exploitation, and strengthen the ability to avoid local optima. Network connectivity constraints are further incorporated into the model to ensure the communication feasibility of the deployment scheme. Simulation results demonstrate that the proposed method can achieve higher coverage, a more uniform node distribution, and better convergence and stability while satisfying connectivity constraints.