Local routing algorithms based on Potts neural networks.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1042234
- author
- Häkkinen, Jari LU ; Lagerholm, Martin LU ; Peterson, Carsten LU and Söderberg, Bo LU
- organization
- publishing date
- 2000
- 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
- 2016-04-01 11:51:35
- date last changed
- 2024-01-07 23:13:45
@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, Martin 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}}, doi = {{10.1109/72.857776}}, volume = {{11}}, year = {{2000}}, }