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Local routing algorithms based on Potts neural networks.

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Author

Summary, in English

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.

Publishing year

2000

Language

English

Pages

970-977

Publication/Series

IEEE Transactions on Neural Networks

Volume

11

Issue

4

Document type

Journal article

Publisher

IEEE - Institute of Electrical and Electronics Engineers Inc.

Topic

  • Computer Engineering

Status

Published

ISBN/ISSN/Other

  • ISSN: 1045-9227