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Local routing algorithms based on Potts neural networks.

Häkkinen, Jari LU ; Lagerholm, M; Peterson, Carsten LU and Söderberg, Bo LU (2000) In IEEE Transactions on Neural Networks 11(4). p.970-977
Abstract
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... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
IEEE Transactions on Neural Networks
volume
11
issue
4
pages
970 - 977
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:18249823
  • scopus:0034230140
ISSN
1045-9227
DOI
10.1109/72.857776
language
English
LU publication?
yes
id
3333b327-0deb-4141-b810-4742bd1b0344 (old id 1042234)
date added to LUP
2008-03-13 16:23:39
date last changed
2017-01-01 04:36:38
@article{3333b327-0deb-4141-b810-4742bd1b0344,
  abstract     = {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.},
  author       = {Häkkinen, Jari and Lagerholm, M and Peterson, Carsten and Söderberg, Bo},
  issn         = {1045-9227},
  language     = {eng},
  number       = {4},
  pages        = {970--977},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Neural Networks},
  title        = {Local routing algorithms based on Potts neural networks.},
  url          = {http://dx.doi.org/10.1109/72.857776},
  volume       = {11},
  year         = {2000},
}