A fast and reliable routing algorithm based on Hopfield Neural Networks optimized by Particle Swarm Optimization

Routing is very important for computer networks because it is one of the main factors that influences network performance. In this paper, we propose an improved intelligent method for routing based on Hopfield Neural Networks (HANN), which uses a discrete equation and the Particle Swarm Optimization...

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Bibliographic Details
Main Authors: Bastos-Filho, C.J.A., Schuler, W.H., Oliveira, A.L.I.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Joints
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Summary:Routing is very important for computer networks because it is one of the main factors that influences network performance. In this paper, we propose an improved intelligent method for routing based on Hopfield Neural Networks (HANN), which uses a discrete equation and the Particle Swarm Optimization (PSO) technique to optimize the HNN parameters. The fitness function for the PSO algorithm used here is a combination of the number of iterations for convergence and the percentage error when the HNN method tries to find the best path in a communication network. The simulation results show that PSO is a reliable approach to optimize the Hopfield network for routing in computer networks, since this method results in fast convergence and produces accurate results.
ISSN:2161-4393
1522-4899
2161-4407
DOI:10.1109/IJCNN.2008.4634189