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

Maganti Srinivas; Dr.M.Ramesh Patnaik

doi:10.6180/jase.202208_25(4).0008

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

Computer Science and Information Engineering

Maganti Srinivas This email address is being protected from spambots. You need JavaScript enabled to view it.^1,2 and Dr.M.Ramesh Patnaik³

¹Research Scholar/Instrument technology, A.U.College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh 530003, India
²Assistant Professor/EIE, V.R.Siddhartha Engineering College, Vijayawada, Andhra Pradesh 520007 India
³Associate Professor/Instrument technology, A.U.College of Engineering, Andhra University, Visakhapatnam, Andhra Pradesh 530003, India

Received: June 26, 2021
Accepted: July 29, 2021
Publication Date: November 1, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0008

ABSTRACT

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 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 EKHO-QBA algorithm, a set of different experiments were carried out and highlighted the supremacy over the compared methods interms of different performance measures.

Keywords: MANET, Clustering, Routing, Krill herd algorithm, Bat algorithm

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