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Local routing algorithms based on Potts neural networks
J Häkkimen1, M Lagerholm, C Peterson
1Complex Systems Group, Department of Theoretical Physics, University of Lund, SE-223 62 Lund, Sweden. jari@thep.lu.se
IEEE Transactions on Neural Networks
|February 6, 2008
Summary
This study introduces a feedback neural network approach for static communication routing in asymmetric networks. The method efficiently minimizes connection costs while respecting capacity constraints, outperforming existing heuristics.
Area of Science:
- Computer Science
- Artificial Intelligence
- Network Engineering
Background:
- Static communication routing in asymmetric networks presents complex challenges.
- Existing methods may lack efficiency or scalability for various routing problems.
Purpose of the Study:
- To develop a unified feedback neural approach for static communication routing.
- To address single unicast, multicast, and multiple multicast problems efficiently.
- To minimize total connection cost under capacity constraints.
Main Methods:
- Utilized a mean field formulation of the Bellman-Ford method as a common platform.
- Developed algorithms for single unicast, multicast, and multiple multicast routing.
- Inherited the locality and update philosophy of the Bellman-Ford algorithm.
Main Results:
- The proposed methods demonstrated superior performance compared to simple heuristics.
- Achieved cost minimization objectives subject to network capacity constraints.
- Computational demands were found to be modest.
Conclusions:
- The feedback neural approach offers an effective solution for static communication routing.
- The method provides a scalable and efficient platform for diverse routing scenarios.
- Results indicate a favorable balance between solution quality and computational cost.
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