An Efficient Cluster Head Selection Approach in MANET using Enhanced Elephant Herding Optimization based on Chaos Theory and the Simplex Method
In Mobile Ad Hoc Networks (MANETs), choosing an efficient cluster-head (CH) is very important to ensure network stability, scalability and communication efficiency. However, existing clustering algorithms are prone to premature convergence, unstable cluster formation, high re-affiliation rates and high control-message overhead under dynamic network conditions. In order to solve these problems, this paper presents a Chaos-Simplex Elephant Herding Optimization (CS-EHO) algorithm for the cluster-head selection in MANETs. The proposed approach employs chaotic population initialization to increase the diversity of search and avoid the local optima, and the simplex-based local search to improve the exploitation capability and the convergence accuracy. The performance of the proposed CS-EHO is compared with GWOCA, EHO, HBA, MPSO, ABC and DGA using four clustering metrics, namely, average number of clusters, cluster lifetime, re-affiliation rate and control-message overhead, for different network sizes, node speeds and transmission ranges. The experimental results show that CS-EHO always outperforms all benchmark algorithms. For a network size of 500 nodes with a transmission range of 100 m, CS-EHO produced only 26 clusters in comparison with 32, 37, 54, 59, 65 and 102 clusters for GWOCA, EHO, HBA, MPSO, ABC and DGA respectively. CS-EHO achieved cluster lifetime of 79% and 80% for transmission ranges of 100 m, 200 m respectively with a low re-affiliation rate of 0.0372 and control overhead of 0.0032 under high mobility (80 km/h). The hybridization of chaotic exploration and simplex based exploitation significantly improves the cluster-head selection ability of EHO. The proposed CS-EHO framework gives rise to more stable clusters, reduces the cost of cluster maintenance, lowers the communication overhead and improves the scalability of the network. Therefore, CS-EHO is an effective and robust clustering solution for highly dynamic MANET scenarios.