An algorithm for solving the large-scale Traveling Salesman Problem is presented. Research into past work in the area of Hopfield neural network use in solving the Traveling Salesman Problem has yielded design ideas that have been incorporated into this work. The algorithm consists of an unsupervised learning algorithm and a recursive Hopfield neural network. The unsupervised learning algorithm was used to decompose the problem into clusters. The recursive Hopfield neural network was applied to the centroids of the clusters, then to the cities in each cluster, in order to find an optimal path. An improvement in both computation speed and solution accuracy is shown by the proposed algorithm over the straight use of the Hopfield neural network.
| Identifer | oai:union.ndltd.org:fiu.edu/oai:digitalcommons.fiu.edu:etd-4530 |
| Date | 06 April 1994 |
| Creators | Figueras, Anthony L. |
| Publisher | FIU Digital Commons |
| Source Sets | Florida International University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | FIU Electronic Theses and Dissertations |
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