LCN '00 Proceedings of the 25th Annual IEEE Conference on Local Computer Networks
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
New Operators of Genetic Algorithms for Traveling Salesman Problem
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
A stochastic foundation of available bandwidth estimation: multi-hop analysis
IEEE/ACM Transactions on Networking (TON)
A Novel Geographic Set-up and an Access Protocol for Mesh, Ad-Hoc and Cognitive Networks
CMC '09 Proceedings of the 2009 WRI International Conference on Communications and Mobile Computing - Volume 01
Genetic Algorithms for Route Discovery
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
This paper addresses the path selection problem from a known source to the destination in dense networks. The proposed solution for route discovery uses the genetic algorithm approach for a QoS-based network. The multi point crossover and mutation helps in determining the optimal path and alternate path when required. The input to the genetic algorithm is a learnt module which is a part of the cognitive router that takes care of four QoS parameters. Here, the set of nodes selected for routing is determined by delay, jitter and loss. On this graded surface of nodes selected, the bandwidth parameter is considered for path selection. The aim of the approach is to occupy the maximised bandwidth along the forward channels and minimise the route length. The population size is considered as fixed nodes participating in the network scenario, which will be limited to a known size of topology. The simulated results show that by using genetic algorithm GA approach the probability of convergence to shortest path is higher.