A Resilient UVC/RF Heterogeneous UAV Network Construction Algorithm Based on SMA-ACO-GWO Hybrid Optimization

To address the loss of high-quality solutions caused by decoupled topology and routing optimization, and the challenge of balancing robustness, delay, energy consumption and throughput in Ultraviolet C/Radio Frequency (UVC/RF) heterogeneous UAV swarm networks under extreme scenarios, this paper proposes a layered hybrid optimization framework termed Slime Mould Algorithm–Ant Colony Optimization–Grey Wolf Optimizer (SMA-ACO-GWO). It integrates the robust topology construction of the slime mould algorithm, the efficient multi-path routing of ant colony optimization, and the strong multi-objective search capability of the grey wolf optimizer. Joint optimization is achieved through four core mechanisms: slime mould topology-guided population initialization, ant colony routing-driven hierarchical leader mechanism, network state-aware dynamic weight adjustment, and topology-routing bidirectional feedback iteration. Simulation results show that compared with the standard GWO algorithm, the proposed algorithm reduces the number of convergence iterations by 60%, achieves the upper limit of 1.00 for the adopted robustness metric, reduces the average end-to-end delay by 86.2%, increases the normalized throughput by 77.9%, and reduces the total energy consumption by 18.0%. The algorithm features a low-complexity centralized architecture with distributed deployment potential. Its main optimization loop has a per-iteration complexity of O(n2), introducing low computational overhead and making it suitable for deployment on low-cost airborne platforms for highly dynamic and confrontational UAV swarm communication missions.