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JRM Vol.17 No.5 pp. 560-567
doi: 10.20965/jrm.2005.p0560
(2005)

Paper:

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Local Clustering Organization (LCO) Solving a Large-Scale TSP

Masashi Furukawa*, Michiko Watanabe**,
and Yusuke Matsumura***

*Department of Information Technology Integration, Asahikawa National College of Technology, 2-2 Shunkodai, Asahikawa 071-8142, Japan

**Technical Official with Ministry of Education and Science IT Promoting Room, Asahikawa National College of Technology, 2-2 Shunkodai, Asahikawa 071-8142, Japan

***Advanced Course of Production System Engineering, Asahikawa National College of Technology, 2-2 Shunkodai, Asahikawa 071-8142, Japan

Received:
May 10, 2005
Accepted:
August 17, 2005
Published:
October 20, 2005
Creative Commons License
Keywords:
self-organizing map, machine learning, traveling salesman problem, neural networks, combinatorial optimization
Abstract
The traveling salesman problem (TSP) is one of the most difficult problems that occur in different types of industrial scheduling situations. We propose a solution, involving local clustering organization (LCO), for a large-scale TSP based on the principle of the self-organizing map (SOM). Although the SOM can solve TSPs, it is not applicable to practical TSPs because the SOM references city coordinates and assigns synapses to coordinates. LCO indirectly uses the SOM principle and, instead of city coordinates, references costs between two cities, to determine the sequence of cities. We apply LCO to a large-scale TSP to determine its efficiency in numerical experiments. Results demonstrate that LCO obtains the desired solutions.
Cite this article as:
M. Furukawa, M. Watanabe, and Y. Matsumura, “Local Clustering Organization (LCO) Solving a Large-Scale TSP,” J. Robot. Mechatron., Vol.17 No.5, pp. 560-567, 2005.
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