Research on Traveling Routes Problems Based on Improved Ant Colony Algorithm

DOI: 10.4236/cn.2013.53B2109   PDF   HTML     3,837 Downloads   5,176 Views   Citations


This paper studies how to obtain a reasonable traveling route among given attractions. Toward this purpose, we propose an objective optimization model of routes choosing, which is based on the improved Ant Colony Algorithm. Furthermore, we make some adjustment in parameters in order to improve the precision of this algorithm. For example, the inspired factor has been changed to get better results. Also, the ways of searching have been adjusted so that the traveling routes will be well designed to achieve optimal effects. At last, we select a series of attractions in Beijing as data to do an experimental analysis, which comes out with an optimum route arrangement for the travelers; that is to say, the models we propose and the algorithm we improved are reasonable and effective.

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Yu, Z. , Zhang, S. , Chen, S. , Liu, B. and Ye, S. (2013) Research on Traveling Routes Problems Based on Improved Ant Colony Algorithm. Communications and Network, 5, 606-610. doi: 10.4236/cn.2013.53B2109.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] C. Cooper, J. Fletcher, D. Gilbert and S. Wanhil, “Tourism Principles and Practice,” Longman, New York, 1998.
[2] C. A. Gunn and T. Var, “Tourism Planning: Basics Concepts Cases,” Routledge, New York, 2002.
[3] A M. Mill, “The Tourism System,” Prentice-Hall, Englewood Cliffs, 1985.
[4] D. Y. Fang, “The Application of Graph Theory in the Selection of Tourist Routes,” Changchun University of Technology, 2009, pp. 582-586.
[5] Z. L. Liu, C. Li and L. Wang, “Design and Evaluation Level Analysis and Graph Theory Model-Based Tours,” Managers, No. 15, 2009, pp. 386-387.
[6] T. Stutzle and H. Hoos, “Max-Min Ant System and Local Search for the Travelling Salesman Problem,” IEEE International Conference on Evolutionary Computation and Evolutionary Programming Conference, 1997, pp. 309-314.
[7] T. Stuezle and M. Dorigo, “A Short Convergence Proof for a Class of Ant Colony Optimization Algorithms,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 4, 2002, pp. 358-365.
[8] M. Dorigo and L. M. Gambardella, “Ant Colonies for the Travelling Salesman Problem,” BioSystems, Vol. 43, No. 2, 1997, pp. 73-81.
[9] M. Dorigo and T. Stutzle, “Ant colony opitimization,” MIT Press, Cambridge, 2004.
[10] W. J. Gutjahr, “A Graph-Based Ant System and Its Convergence,” Future Generation Computer System, Vol. 16, No. 8, 2000, pp. 873-888.
[11] K. Dai, S. W. Lu and X. G. Jiang, “Genetic Algorithm Based Solution of Multiple Travelling Salesman Problem,” Computer Engineering, Vol. 30, No. 16, 2004, pp. 139-145.
[12] J. H. Yoo, R. J. La and A. M. Makowski, “Convergence of Ant Routing Algorithms—Results for Simple Parallel Network and Perspectives,” Technical Report CSHCN 2003-44, Institute for Systems Research, University of Maryland, College Park (MD), 2003.
[13] Y. Zhang, “An Improved Ant Colony Optimization Algorithm Based on Route Optimization and Its Applications in Travelling Salesman Problem,” IEEE, BIBE, 2007.
[14] L.Y. Li and Y. Xiang, “Research of Multi-Path Touting Protocol Based on Parallel Ant Colony Algorithm Optimization in Mobile Ad Hoc Networds,” 5th International Conference on Information Technology: New Generations, 2008.

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