An Intelligent Harris Hawks Optimization Based Cluster Optimization Scheme for VANETs

In recent years, intelligent vehicles with cutting-edge vehicular applications have grown in popularity, enabling the growth of Vehicular Ad hoc Networks (VANETs). (VANETs) are a network that share and analyze real-time data require well-organized efficient delivery method. The stability clusters dynamic topology change VANETs major issues finding an optimal route amongst vehicles. cooperative approach surprise pounce chasing technique Harris Hawks nature serve as main sources inspiration for Optimization. this technique, several hawks work together to attack victim from various angles it. Due unpredictable situations prey’s fleeing movements, can exhibit variety strategies. This study proposes novel clustering optimization takes into account communication range, number nodes, velocity, orientations, grid size. To create evaluate ideal cluster head (CH), proposed method is based on Intelligent Optimization Algorithm Clustering (iCHHO) which finds reliable routes Other state-of-the-art methods, such Grasshopper (GOA), Gray Wolf (GWO), Whale (WOACNET), utilized validate Our findings show developed outperforms other current methods terms clusters, variable ranges, size, Furthermore, statistical analysis concludes improves by 79% increases adjusted R -squared 91.22.