🤖 AI Summary
To address the challenges of complex parameter configuration and poor dynamic adaptability in vehicular ad hoc network (VANET) communication protocols, this paper proposes a cross-layer cooperative adaptive tuning framework based on a hybrid metaheuristic algorithm. For the first time, genetic algorithm (GA) and particle swarm optimization (PSO) are synergistically integrated to jointly optimize critical parameters at both the MAC and routing layers. The framework is implemented and evaluated in the NS-2 simulator using an IEEE 802.11p-based VANET model. Experimental results under high-mobility scenarios demonstrate significant performance improvements: end-to-end packet delivery ratio increases by 32%, average throughput rises by 27%, and end-to-end delay decreases by 41%. These results validate the framework’s robustness and effectiveness in dynamically varying channel conditions and network topologies.