Abstract
The use of neural network computational algorithms to determine optimal traffic routing for communication networks is introduced. The routing problem requires choosing multilink paths for node-to-node traffic to minimize loss, which is represented by expected delay or some other function of traffic. The minimization procedure is implemented using a modification of the neural network traveling-salesman algorithm. Illustrative simulation results on a minicomputer show reasonable convergence in 250 iterations for a 16-node network with up to four links from origin to destination.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Node (physics)Routing (electronic design automation)Artificial neural networkComputer scienceConvergence (economics)Travelling salesman problemComputer networkGeographic routingMultipath routingStatic routingRouting protocolArtificial intelligenceAlgorithmEngineering
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Publication Info
- Year
- 1988
- Type
- article
- Volume
- 8
- Issue
- 2
- Pages
- 26-31
- Citations
- 174
- Access
- Closed
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Cite This
Herbert E. Rauch,
T. Winarske
(1988).
Neural networks for routing communication traffic.
IEEE Control Systems Magazine
, 8
(2)
, 26-31.
https://doi.org/10.1109/37.1870
Identifiers
- DOI
- 10.1109/37.1870