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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

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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.

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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.