Optimal path selection in Graded network using Artificial Bee Colony algorithm with Agent enabled Information

TL;DR

This paper presents a novel routing approach in graded networks using the Artificial Bee Colony algorithm, which efficiently finds optimal paths based on bandwidth and node direction, outperforming traditional methods.

Contribution

It introduces an agent-enabled, quadrant-based ABC routing method for graded networks, improving path selection speed and efficiency over non-graded networks.

Findings

Path convergence was 30% faster in graded networks using ABC.

The approach effectively utilizes QoS parameters for optimal path selection.

Quadrant-based search reduces the search space and improves routing efficiency.

Abstract

In this paper we propose a network aware approach for routing in graded network using Artificial Bee Colony (ABC) algorithm. ABC has been used as a good search process for optimality exploitation and exploration. The paper shows how ABC approach has been utilized for determining the optimal path based on bandwidth availability of the link and how it outperformed non graded network while deriving the optimal path. The selection of the nodes is based on the direction of the destination node also. This would help in narrowing down the number of nodes participating in routing. Here an agent system governs the collection of QoS parameters of the nodes. Also a quadrant is synthesized with centre as the source node. Based on the information of which quadrant the destination belongs, a search is performed. Among the many searches observed by the onlooker bees the best path is selected based on which onlooker bee comes back to source with information of the optimal path. The simulation result shows that the path convergence in graded network with ABC was 30% faster than non-graded ABC.