Competitive performance analysis of two evolutionary algorithms for routing optimization in graded network

TL;DR

This paper compares Artificial Bee Colony and Genetic Algorithms for routing optimization in graded networks, showing ABC's superior speed and potential for future cognitive routing applications.

Contribution

It demonstrates the effectiveness of ABC over GA in network routing optimization, particularly in convergence speed and path quality based on QoS metrics.

Findings

ABC speeds up local minimum search convergence by 60% compared to GA.

ABC finds higher quality paths based on bandwidth availability.

Potential for implementing cognitive routing in future networks.

Abstract

In this paper we compare the two intelligent route generation system and its performance capability in graded networks using Artificial Bee Colony (ABC) algorithm and Genetic Algorithm (GA). Both ABC and GA have found its importance in optimization technique for determining optimal path while routing operations in the network. The paper shows how ABC approach has been utilized for determining the optimal path based on bandwidth availability of the links and determines better quality paths over GA. Here the nodes participating in the routing are evaluated for their QoS metric. The nodes which satisfy the minimum threshold value of the metric are chosen and enabled to participate in routing. A quadrant is synthesized on the source as the centre and depending on which quadrant the destination node belongs to, a search for optimal path is performed. The simulation results show that ABC speeds up local minimum search convergence by around 60% as compared to GA with respect to traffic intensity, and opens the possibility for cognitive routing in future intelligent networks.