Abstract:

This article introduces a hybrid GA–BO–DRL optimization framework designed to enhance ground‑node coverage in UAV‑enabled wireless mesh networks. The proposed methodology integrates a global search mechanism based on a genetic algorithm, a Bayesian optimization module for local refinement, and a deep reinforcement learning agent enabling real‑time adaptation to node mobility. The combined approach maximizes coverage, minimizes the number of active UAV platforms, and maintains network robustness under dynamic conditions. Simulation results demonstrate significant improvements in coverage efficiency and energy conservation, underscoring the method’s applicability to time‑critical scenarios such as disaster‑relief operations and military deployments.