Clustering in Mobile Ad Hoc Networks Using Comprehensive Learning Particle Swarm Optimization (CLPSO)

In this work, we propose a Comprehensive Learning Particle Swarm Optimization (CLPSO) based weighted clustering algorithm for mobile ad hoc networks. It finds the optimal number of clusters to efficiently manage the resources of the network. The proposed CLPSO based clustering algorithm takes into consideration the ideal degree, transmission power, mobility, and battery power of the mobile nodes. A weight is assigned to each of these parameters of the network. Each particle contains information about the cluster-heads and the members of each cluster. The simulation results are compared with two other well-known clustering algorithms. Results show that the proposed technique works better than the other techniques especially in dense networks.