Adaptive Mode-Aware Swarm Hybrid Optimization for Heterogeneous UAV-Assisted IoV-MEC Networks

UAV-mounted Mobile Edge Computing (MEC) can extend vehicular computing where terrestrial infrastructure is sparse, but dense traffic and limited UAV batteries make resource management difficult. We consider a swarm in which a base-station-connected Head-UAV coexists with a flying ad hoc network of Member-UAVs and propose Adaptive Mode-Aware Swarm Hybrid (A-MASH), which jointly decides vehicle association with admission control, MEC CPU allocation, uplink power and UAV-to-UAV relaying. A battery- and load-aware ant colony search over the discrete decisions is coupled with a Grey Wolf Optimizer over the continuous resources; the Head-UAV access mode follows load, battery and interference-cancellation quality, and a Kalman-filter channel predictor supports decoding. In slot-level simulations with 10–150 vehicles and equal evaluation budgets, A-MASH delivers 65–102% more tasks on time than standalone metaheuristics at 100 vehicles and matches an alternating-optimization baseline up to 100 vehicles, but simple model-based heuristics are better once the load exceeds the computing capacity of the swarm. The predictor adds up to 5.5 percentage points of delivery ratio, NOMA helps only with near-perfect interference cancellation, and the battery-aware mechanisms postpone the first UAV withdrawal by one scheduling epoch.