Determining Cluster-Heads in Mobile Ad-Hoc Networks Using Multi-Objective Evolutionary based Algorithm

A mobile ad-hoc network (MANET), a set of wirelessly connected sensor nodes, is a dynamic system that executes hop-by-hop routing independently with no external help of any infrastructure. Proper selection of cluster heads can increase the life time of the Ad-hoc network by decreasing the energy consumption. Although different methods have been successfully proposed by researchers to tackle this problem, nearly all of them have the deficiency of providing a single combination of head clusters as the solution. On the contrary, in our proposed method, using a Multi-Objective Genetic Algorithm, a set of near optimum solutions is provided. In the proposed method, energy consumption, number of cluster heads, coverage and degree difference are considered as objectives. Numerical results reveal that the proposed algorithm can find better solutions when compared to conventional methods in this area namely, weighted clustering algorithm (WCA), comprehensive learning particle swarm optimization (CLPSO) and multi objective particle swarm optimization(MOPSO).