MRMH: Multi-Constraint Routing Optimization Using Hybrid Metaheuristics in Vehicular Ad-Hoc Networks

Advancements in Vehicular Ad-Hoc Networks (VANETs) are crucial for the next generation of intelligent transportation systems. Addressing the complexities of routing and clustering in these networks, our research introduces the Multi-constraint Routing Mechanism using Hybridization (MRMH). This approach innovatively combines the strengths of Grey Wolf Optimization (GWO) and Sequential Quadratic Programming (SQP). While GWO is adept at global search, its tendency for premature convergence is effectively countered by SQP's excellence in nonlinear constraint management and local optimization. MRMH further benefits from a novel weighted distance approach and a nonlinear decay formulation, enhancing the balance between exploration and exploitation phases in VANET optimization. Our fitness function, designed around essential VANET metrics like inter-cluster distance and vehicle orientation, ensures the applicability of MRMH in real-world scenarios. Empirical results from extensive simulations exhibit MRMH's superiority, showing significant improvements in key performance indicators such as transmission delay (16% reduction), packet delivery ratio (12% increase), throughput (18% increase), cluster stability (23% improvement), and convergence rate (11% increase). These findings are backed by rigorous statistical analyses, confirming MRMH's potential in VANETs.