A Potts Neuron Approach to Communication Routing
Author
Summary, in English
A feedback neural network approach to communication routing problems
is developed, with emphasis on Multiple Shortest Path problems,
with several requests for transmissions between distinct start- and
endnodes. The basic ingredients are a set of Potts neurons for each request,with interactions designed to minimize path lengths and to
prevent overloading of network arcs. The topological nature of the
problem is conveniently handled using a propagator matrix approach.
Although the constraints are global, the algorithmic steps are based
entirely on local information, facilitating distributed implementations.
In the polynomially solvable single-request case, the approach reduces
to a fuzzy version of the Bellman-Ford algorithm.
The method is evaluated for synthetic problems of varying sizes and
load levels, by comparing to exact solutions from a branch-and-bound
method, or to approximate solutions from a simple heuristic.
With very few exceptions, the Potts approach gives legal solutions of
very high quality. The computational demand scales merely as the
product of the numbers of requests, nodes, and arcs.
is developed, with emphasis on Multiple Shortest Path problems,
with several requests for transmissions between distinct start- and
endnodes. The basic ingredients are a set of Potts neurons for each request,with interactions designed to minimize path lengths and to
prevent overloading of network arcs. The topological nature of the
problem is conveniently handled using a propagator matrix approach.
Although the constraints are global, the algorithmic steps are based
entirely on local information, facilitating distributed implementations.
In the polynomially solvable single-request case, the approach reduces
to a fuzzy version of the Bellman-Ford algorithm.
The method is evaluated for synthetic problems of varying sizes and
load levels, by comparing to exact solutions from a branch-and-bound
method, or to approximate solutions from a simple heuristic.
With very few exceptions, the Potts approach gives legal solutions of
very high quality. The computational demand scales merely as the
product of the numbers of requests, nodes, and arcs.
Department/s
Publishing year
1998
Language
English
Pages
1587-1599
Publication/Series
Neural Computation
Volume
10
Document type
Journal article
Publisher
MIT Press
Topic
- Computer Engineering
Status
Published
ISBN/ISSN/Other
- ISSN: 1530-888X