Fast TSP algorithm based on binary neuron output and analog neuron input using the zero-diagonal interconnect matrix and necessary and sufficient constraints of the permutation matrix
- 1 January 1988
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 259-266 vol.2
- https://doi.org/10.1109/icnn.1988.23937
Abstract
Alleged difficulties of the original Hopfield and Tank (H-T) neural network model reported by G.V. Wilson and G.S. Pawley (1988) in attempting a scaled-up VLSI implementation of the traveling salesman problem (TSP) are clarified and repudiated. A simple refinement is presented that has sped up and eliminated the decaying dynamics compounded by the feeble and indecisive analog neurons having a self-decaying interconnect. In summary, the modified TSP version is based on binary neuronic output, analog neuronic input, a zero diagonal interconnect matrix, the necessary and sufficient constraint of a permutation matrix, Lagrangian multipliers a=b=c=1, and Euler's first-order integration with the step constant about 10/sup -4/. Programs, one written in True Basic running on a microcomputer (Macintosh Plus or Mac II) and the other written in C on a mainframe computer, are briefly mentioned.Keywords
This publication has 7 references indexed in Scilit:
- Constraint optimization neural network for adaptive early visionNeural Networks, 1988
- Resource allocation using a constraint optimizing adaptive neural networkNeural Networks, 1988
- On the stability of the Travelling Salesman Problem algorithm of Hopfield and TankBiological Cybernetics, 1988
- The capacity of the Hopfield associative memoryIEEE Transactions on Information Theory, 1987
- Fast simulated annealingPhysics Letters A, 1987
- “Neural” computation of decisions in optimization problemsBiological Cybernetics, 1985
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences, 1982