Local routing algorithms based on Potts neural networks

A feedback neural approach to static communication routing in asymmetric networks is presented, where a mean field formulation of the Bellman-Ford method for the single unicast problem is used as a common platform for developing algorithms for multiple unicast, multicast and multiple multicast probl...

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Bibliographic Details
Published in:IEEE Transactions on Neural Networks Vol. 11; no. 4; pp. 970 - 977
Main Authors: Hakkinen, J., Lagerholm, M., Peterson, C., Soderberg, B.
Format: Journal Article
Language:English
Published: United States IEEE 01.07.2000
Subjects:
Algorithms
Computer and Information Sciences
Computer Engineering
Computer networks
Cost engineering
Costs
Data- och informationsvetenskap (Datateknik)
Datorteknik
Distributed algorithms
Distributed computing
Encoding
Heuristic
Mathematical models
Multicast
Multicast algorithms
Natural Sciences
Naturvetenskap
Neural networks
Neurofeedback
Neurons
Routing
Routing (telecommunications)
Unicast
ISSN:1045-9227, 2162-2388, 1941-0093
Online Access:Get full text
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Summary:A feedback neural approach to static communication routing in asymmetric networks is presented, where a mean field formulation of the Bellman-Ford method for the single unicast problem is used as a common platform for developing algorithms for multiple unicast, multicast and multiple multicast problems. The appealing locality and update philosophy of the Bellman-Ford algorithm is inherited. For all problem types the objective is to minimize a total connection cost, defined as the sum of the individual costs of the involved arcs, subject to capacity constraints. The methods are evaluated for synthetic problem instances by comparing to exact solutions for cases where these are accessible, and else with approximate results from simple heuristics. In general, the quality of the results are better than those of the heuristics. Furthermore, the computational demands are modest, even when the distributed nature of the the approach is not exploited numerically.
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ISSN:1045-9227
2162-2388
1941-0093
DOI:10.1109/72.857776