MRMH: Multi-Constraint Routing Optimization Using Hybrid Metaheuristics in Vehicular Ad-Hoc Networks
Published in , 2025
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 functi
Recommended citation: 0
Recommended citation: 0
Download Paper