A hybrid crow search and Harris Hawks optimization approach for clustering based routing in vehicular ad hoc networks

Abstract

Vehicular Ad Hoc Networks (VANETs) are characterized by highly dynamic topologies, leading to frequent link breakages and challenging reliable routing. While clustering effectively mitigates topology instability, optimal Cluster Head (CH) selection and routing remain NP-hard problems. Despite various existing approaches, many current meta-heuristic routing protocols struggle to balance exploration and exploitation in highly dynamic VANET environments, often suffering from premature convergence and cluster instability under high mobility. To address these critical limitations, this paper proposes CRAHO, a novel hybrid meta-heuristic approach integrating the CSA and HHO for robust clustering-based routing in VANETs. Specifically, CSA is employed during the clustering phase to evaluate critical parameters—such as communication link quality and spatial distance—to form highly stable clusters. Subsequently, the routing phase leverages HHO based on distance metrics and node degrees to establish optimal, persistent inter-cluster paths. By formulating a comprehensive multi-objective fitness function, the CRAHO algorithm effectively coordinates exploration and exploitation. This approach guarantees QoS by minimizing routing overhead and end-to-end delay while maximizing the Packet Delivery Ratio (PDR). Simulation results demonstrate that the proposed CRAHO framework significantly outperforms benchmark routing protocols in maintaining network stability and optimizing data transmission in highly mobile vehicular environments. Specifically, compared to the baseline methods, CRAHO achieves improvements of 10.06% in network lifetime, 10.65% in throughput, 6.72% in PDR, and a 6.41% reduction in end-to-end delay.