Weighted Clustering Using Comprehensive Learning Particle Swarm Optimization for Mobile Ad Hoc Networks
A mobile Ad-hoc network consists of dynamic nodes that can move freely. These nodes communicate with each other without a base station. In this paper, the authors propose a Comprehensive Learning Particle Swarm Optimization (CLPSO) based clustering algorithm for mobile ad hoc networks. It has the ability to find the optimal or near-optimal number of clusters to efficiently manage the resources of the network. The cluster-heads do the job of routing network packets within the cluster or to the nodes of other clusters. The proposed CLPSO based clustering algorithm takes into consideration the transmission power, ideal degree, mobility of the nodes and battery power consumption of the mobile nodes.