Performance Evaluation of Bio-Inspired Hybrid GOA-WOA Algorithm for Clustering and Routing in Vehicular Ad-Hoc Networks (VANETs)

This paper presents a performance evaluation of a novel hybrid bio-inspired algorithm for clustering and routing optimization in Vehicular Ad-Hoc Networks (VANETs). The proposed approach integrates the Grasshopper Optimization Algorithm (GOA) and Whale Optimization Algorithm (WOA) to enhance clustering efficiency, while incorporating established routing protocols such as Ad-hoc On-Demand Distance Vector (AODV) and Greedy Perimeter Stateless Routing (GPSR) to improve overall network performance. The hybrid GOA-WOA clustering algorithm leverages the exploration capabilities of GOA and the exploitation strengths of WOA to achieve optimal cluster formation. This combination aims to balance cluster stability and adaptability in the highly dynamic VANET environment. The clustering results are then utilized by AODV and GPSR routing protocols to establish efficient communication paths between vehicles. Performance is assessed using a Manhattan grid-based mobility model, evaluating metrics like average cluster size, number of clusters, packet delivery ratio, and latency under various node densities and transmission ranges. Simulation results show that this hybrid approach significantly enhances VANET performance compared to traditional methods, particularly in urban scenarios with varying vehicle densities. The proposed hybrid GOA-WOA achieves average 3% improvement in packet delay ratio (PDR) compared to traditional methods. The GOA-WOA clustering improves stability, while AODV and GPSR benefit from optimized cluster structures, reducing routing overhead. The algorithm's adaptive nature maintains optimal performance across different network conditions, demonstrating potential for real-world VANET applications with artificial intelligence (AI) algorithms.