Effective Routing Using Multi-Objective Levy flight- Artificial Rabbit Optimization Algorithm for Wireless Mesh Networks

Abstract

Wireless mesh networks (WMNs) are familiar due to their unique characteristics such as adaptability, flexibility and less time for transmitting data packets. The routing technique acts a significant role in transmitting data among the nodes. Moreover, the routing technique is mainly responsible to improve the performance of WMNs with various applications. With the rapid development of various applications, energy, and distance are fundamental aspects which aid in next-level communication. But, recent research has not provided a clear idea of improving the transmission quality by offering an optimal route. These challenges are overwhelmed using the proposed multiobjective levy flight artificial rabbit optimization algorithm (ML-AROA) for energy and distance aware (EDA) route discovery. The proposed ML-AROA is an improvisation of the ARO algorithm where the Levy flight technique is implemented to create a random number in a regular manner which is characterized by the generation of arbitrary numbers and helps to jump out the local solutions. Moreover, the convergence accuracy gets enhanced by offering flexibility to detect an optimal route for the transmission of data packets. The results obtained from the experiment shows that the suggested ML-AROA attained better network throughput of 5 12 10  Kbps which is comparatively higher than load balance and interference avoid-partially overlapped channels assignment (LBIA-POCA) framework and multi-objective dyna Q based routing (MODQR) technique.

Swarm Bipolar Algorithm: A Metaheuristic Based on Polarization of Two Equal Size Sub Swarms

“…The red deer algorithm has been combined with deep learning to detect and classify diseases in agricultural plants [5]. Artificial rabbit optimization has been utilized to improve the routing process in wireless mesh networks [6]. Coot optimization has been used to optimize the task classification and scheduling in the cloud computing system to reduce the make-span [7].…”