Genetic Algorithm Based Optimization of Clustering in Ad Hoc Networks

TL;DR

This paper presents a genetic algorithm-based approach to optimize cluster head selection in ad hoc networks, aiming to improve connectivity over traditional deterministic methods.

Contribution

It introduces a new GA-based algorithm for weighted clustering that minimizes cluster heads based on multiple parameters, enhancing network connectivity.

Findings

GA improves connectivity compared to deterministic WCA.

GA does not always outperform deterministic methods.

Performance varies depending on network conditions.

Abstract

In this paper, we have to concentrate on implementation of Weighted Clustering Algorithm with the help of Genetic Algorithm (GA).Here we have developed new algorithm for the implementation of GA-based approach with the help of Weighted Clustering Algorithm (WCA) (4). ClusterHead chosen is a important thing for clustering in adhoc networks. So, we have shown the optimization technique for the minimization of ClusterHeads(CH) based on some parameter such as degree difference, Battery power (Pv), degree of mobility, and sum of the distances of a node in adhoc networks. ClusterHeads selection of adhoc networks is an important thing for clustering. Here, we have discussed the performance comparison between deterministic approach and GA based approach. In this performance comparison, we have seen that GA does not always give the good result compare to deterministic WCA algorithm. Here we have seen connectivity (connectivity can be measured by the probability that a node is reachable to any other node.) is better than the deterministic WCA algorithm (4).