Fuzzy Extended Krill Herd Optimization with Quantum Bat Algorithm for Cluster Based Routing in Mobile Adhoc Networks

MANET integrates a set of autonomous mobile nodes which move independently and send data through wireless links. Clustering and routing are the commonly employed energy-efficient techniques, which can be treated as an NP hard problem and is resolved by computational intelligence algorithms. The mobility of the nodes leads to repeated link failures and low energy efficiency. In order to achieve high energy efficiency and network connectivity, this paper presents a new Fuzzy Extended Krill Herd Optimization with Quantum Bat algorithm (FEKHO-QBA) for Cluster Based Routing in MANET. The presented model uses FEKHO algorithm by integrating the concepts of fuzzy logic and KHO algorithm for the clustering process and effective selection of cluster heads (CHs). Besides, the QBA is applied as a routing technique to determine the optimal paths to the destination nodes. The QBA involves the features of faster convergence rate, easier to implement, and improved accurateness. The application of FEKHO-QBA algorithm offers maximum energy efficiency and network longevity. For determining the effectual performance of the FEKHO-QBA algorithm, a set of different experiments were carried out and highlighted the supremacy over the compared methods interms of different performance measures.