Construction of a stable vehicular ad hoc network based on hybrid genetic algorithm

In vehicular ad hoc networks, the vehicle speed can exceed 120 kmph. Therefore, any node can enter or leave the network within a very short time. This mobility adversely affects the network connectivity and decreases the life time of all established links. To overcome these problems, many routing protocols based clustering technique have been proposed. Indeed, the poor assignment of vehicles to clusters is the most important shortcomings where an inaccurate affiliation may reduce the algorithm’s effectiveness and disrupt all results and analyzes. Therefore, in this paper, we used a hybrid genetic algorithm to improve the cluster maintenance phase in our Weighted K-medoid Clustering Algorithm (WKCA) proposed recently. The proposed model incorporated the tabu search within genetic algorithm to allow the scan of all search space and to reach the best solution without falling into the local optima. This model improves the assignment of nodes to clusters, which in turn achieves efficient vehicle communication and ensures more stability in clustered architecture. Based on relevant metrics, the results obtained in a simulation game show that the enhanced model (E-WKCA) achieves more stability and robustness when compared to the original algorithm (WKCA) and other approaches designed for the same objective.