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Volume 2017, Article ID 9562125, 6 pages
Research Article

Can the Agent with Limited Information Solve Travelling Salesman Problem?

Graduate School of Natural Science and Technology, Okayama University, Okayama, Japan

Correspondence should be addressed to Tomoko Sakiyama; moc.liamg@amayikas.kmt

Received 4 January 2017; Revised 14 March 2017; Accepted 29 March 2017; Published 11 April 2017

Academic Editor: Sergio Gómez

Copyright © 2017 Tomoko Sakiyama and Ikuo Arizono. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems.