A novel hybrid GA-ACO based clustering algorithm for VANET

A novel clustering technique is introduced in this paper that blends genetic algorithm (GA) and ant colony optimization (ACO) for the VANET environment to strengthen the lifetime and consistency of the network topography. The searching ability of genetic algorithms is exploited with the aim of searching suitable cluster centers in the search space and then ACO is employed to achieve the most optimal solution with the aim of reducing the total number of clusters. This algorithm is conveniently implemented with a very low computational complexity. The attainment of the proposed algorithm is compared empirically with prominent population-based algorithms like Comprehensive Learning Particle Swarm Optimization (CLPSO), Multi-Objective Particle Swarm Optimization (MOPSO) and Ant Colony Optimization (ACO) Based Clustering Algorithm (CACONET) with a reduced average number of clusters by 30%, and increased average cluster lifetime by 25%.