A novel weighted clustering algorithm in mobile ad hoc networks using discrete particle swarm optimization (DPSOWCA)

In this paper, a novel weighted clustering algorithm in mobile ad hoc networks using discrete particle swarm optimization (DPSOWCA) is proposed. The proposed algorithm shows how discrete particle swarm optimization can be useful in enhancing the performance of clustering algorithms in mobile ad hoc networks. Consequently, it results in the minimum number of clusters and hence minimum cluster heads. The goals of the algorithm are to minimize the number of cluster heads, to enhance network stability, to maximize network lifetime, and to achieve good end-to-end performance. Analysis and simulation of the algorithm have been implemented and the validity of the algorithm has been proved. Results show that the proposed algorithm performs better than the existing weight-based clustering algorithm and adapts to different kinds of network conditions.

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