Improved Grey Wolf Optimization With Variable Weights For Vanets Clustering Algorithm

Vehicular ad hoc networks (vanets) are wireless communications networks that use low-tech wireless technologies to create unstable networks among road users. these networks are designed to make the connection between vehicle control and road traffic control. congestion-based collection optimization is used to discovery near-optimal solutions since network clustering has a difficult np problem. in this paper, the optimization of improved gray wolf with variable weight-based clustering algorithm for vanets is presented, which mimics the social behaviors and hunting mechanisms of gray wolves to create efficient clusters. the simulation results show that the proposed framework performs better than the previous works compared to the number of group heads with different communication amplitudes, amount of bulges and network size. minimizes the cost of routing for the entire network connection by efficiently reducing the number of clusters required. fewer clusters also reduce the need for resources in the vehicle network.