Stability-based multi-objective clustering in mobile ad hoc networks

In this paper, we propose a stability-based multi-objective clustering algorithm, which can achieve stable cluster structure by exploiting the node movement proximity, and meanwhile optimize multiple clustering metrics simultaneously by a reputable multi-objective evolutionary algorithm (MOEA). The performance of the proposed algorithm has been evaluated through extensive simulations with network topologies of various sizes. The results demonstrated that the clustered topologies generated by our algorithm have good performance in terms of stability. Our algorithm can achieve optimal cluster structure with respect to each clustering metric on small-scale network topology. For large-scale network topology, it also outperforms WCA, a well known multi-objective clustering algorithm using a weighted sum of multiple metrics.