Abstract
The classic TSP problem was researched on and CHN144 was chosen to be the data for research. The method that combined MDP and K-medoids was proposed to solve TSP problem in this paper. First of all, cluster CHN144 data through K-medoids and find out the representative objects respectively. Furthermore, the simple TSP problem that consists of representative objects was solved to acquire the optimal path through the Markov Decision Process. Finally, the global optimal path was acquired as 30445km by using the solution above iteratively to the clustering of each object respectively. The feasibility and superiority of this method was proved by analyzing the experiments we conducted in this paper.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Johnson, D.S., Papadimitriou, C.H., Yannakakis, M.: How easy is local search? Journal of Computer and System Science 37(1), 79–100 (1988)
Papadimitfiou, C.H., Yannakakis, M.: Optimization, approximation and complexity classes. Journal of Computer and System Science. 43(3), 425–440 (1991)
Xiong, S., Li, C.: Distributed Evolutionary Algorithms to TSP Based on Cluster of PC. Microcomputer System 24(6), 959–961 (2003)
Sun, H.-C., Wang, Y.-F.: A Method of Artificial Intelegence-Human-Computer Exchange for Solving Traveling Salesman Problem. Systems Engineering–Theory & Practice 20(5), 1–10 (2000)
Hu, X., Huang, X.: Solving Traveling Salesman Problem with Characteristic of Clustering by Parallel Genetic Algorithm. Computer Engineering and Application 35(1), 66–68 (2004)
Park, H.S., Jun, C.H.: A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications 36(2), 3336–3341 (2009)
Xia, N.-X., Su, Y.-D., Qin, X.: Efficient K-medoids clustering algorithm. Application Research of Computers 27(12), 4517–4519 (2010)
Zhu, M.: Data Mining. Press of Science and Technology, University of China, Hefei (2002)
Derman: Finite state markov decision processes. Academic Press, New York (1970)
Liu, K.: Practical Markov Decision Process. Press of Tsinghua University, Beijing (2004)
Cai, Z.-H., Peng, J.-G., Gao, W., Wei, W., Kang, L.-S.: An Improved Evolutionary Algorithm for the Traveling Salesman Problem. Chinese Journal of Computers 28(5), 823–828 (2005)
Tao, L.-M., Guo, J.-E.: Application of solving TSP based on improved genetic algorithm. Computer Engineering and Application 45(33), 45–47 (2009)
Liu, H., Xiong, S.: Evolutionary Algorithms of TSP Based on Fuzzy C-Means Clustering. Computer Engineering and Applications 42(8), 53–55 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, J., Li, C., Ji, C. (2012). Study of MDP and K-mediods for TSP Problem. In: Zhao, M., Sha, J. (eds) Communications and Information Processing. Communications in Computer and Information Science, vol 289. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31968-6_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-31968-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31967-9
Online ISBN: 978-3-642-31968-6
eBook Packages: Computer ScienceComputer Science (R0)