An Investigation into the improvement of Local Minima of the Hopfield Network

Peng, Mengkang; Gupta, Naren K; Armitage, Alistair

Abstract

The paper investigates the improvement of local minima of the Hopfield network. A local minima escape algorithm (LME algorithm), is proposed for improving local minima of small-scale networks. Experiments on travelling salesman problems (TSP) show that the LME algorithm is an efficient algorithm in improving the local minima, and the comparison with the simulated annealing algorithm (SA) shows that the LME algorithm can produce better results in less time. The paper then investigates the improvement of local minima of large-scale networks. By combining the LME algorithm with a network partitioning technique, a network partitioning algorithm (NPA) is proposed. Experiments on 51 and 101-city TSP problems show that the local minima of large-scale networks can be greatly improved by the NPA algorithm, however, the global minima are still difficult to achieve.

Citation

Peng, M., Gupta, N. K., & Armitage, A. (1996). An Investigation into the improvement of Local Minima of the Hopfield Network. Neural Networks, 9(7), 1241-1253. https://doi.org/10.1016/0893-6080%2896%2900017-2

• Journal Article Type: Article
• Publication Date: 1996-10
• Deposit Date: Jul 29, 2010
• Journal: Neural Networks
• Print ISSN: 0893-6080
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 7
• Pages: 1241-1253
• DOI: https://doi.org/10.1016/0893-6080%2896%2900017-2
• Keywords: Hopfield network; local minima; global minimum; network partitioning; travelling salesman problem
• Public URL: http://researchrepository.napier.ac.uk/id/eprint/3211
• Publisher URL: http://dx.doi.org/10.1016/0893-6080(96)00017-2