"Conscientious" neural nets for tour construction in the traveling salesman problem: the vigilant net

Abstract Previous research has established the feasibility of applying adaptive neural network approaches to the traveling salesman problem. Such methods (unlike the Hopfield network) gradually adjust a ring of nodes until the nodes correspond to actual data points, making them comparable to tour construction heuristics. However, they typically require a few thousand iterations and may not yield “node separation”—a one-to-one correspondence between nodes and cities—in a reasonable processing time. The vigilant net addresses these shortcomings by utilizing a hardware implementable mechanism-that of vigilance, from adaptive resonance theory networks—to help separate nodes without excessive processing. Further, for these solution methods to be truly viable, they must not demand fine-tuning of various parameters, as presently they do. Finally, an advantage to using these methods must be established. Results from previous research and new insights help determine appropriate parameter settings and strategies for the algorithm. Two strategies for selecting the important vigilance parameter are investigated here. In one, feedback from the network helps to adjust vigilance. Results indicate that the vigilant network can yield acceptable results in very short processing times, and the two strategies perform virtually.identically. Comparisons with conventional heuristics yield further insights.

[1]  Stephen Grossberg,et al.  Art 2: Self-Organization Of Stable Category Recognition Codes For Analog Input Patterns , 1988, Other Conferences.

[2]  Richard Durbin,et al.  An analogue approach to the travelling salesman problem using an elastic net method , 1987, Nature.

[3]  X. Xu,et al.  Effective neural algorithms for the traveling salesman problem , 1991, Neural Networks.

[4]  Eugene L. Lawler,et al.  Traveling Salesman Problem , 2016 .

[5]  Laura I. Burke,et al.  The guilty net for the traveling salesman problem , 1992, Comput. Oper. Res..

[6]  Brian W. Kernighan,et al.  An Effective Heuristic Algorithm for the Traveling-Salesman Problem , 1973, Oper. Res..

[7]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[8]  Bernard Angéniol,et al.  Self-organizing feature maps and the travelling salesman problem , 1988, Neural Networks.

[9]  John J. Bartholdi,et al.  Spacefilling curves and the planar travelling salesman problem , 1989, JACM.

[10]  John E. Moody,et al.  Fast adaptive k-means clustering: some empirical results , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[11]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[12]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[13]  Laura I. Burke,et al.  Neural methods for the traveling salesman problem: Insights from operations research , 1994, Neural Networks.