American Journal of Information Science and Computer Engineering
Articles Information
American Journal of Information Science and Computer Engineering, Vol.5, No.2, Jun. 2019, Pub. Date: May 28, 2019
Ant Colony Algorithm for Travel Route Planning
Pages: 66-71 Views: 250 Downloads: 102
[01] Xiaoyang Zheng, Institute of Liangjiang Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
[02] Fengsi Yu, College of Science, Chongqing University of Technology, Chongqing, China.
[03] Guyue Tian, College of Science, Chongqing University of Technology, Chongqing, China.
[04] Liqiong Qiu, College of Science, Chongqing University of Technology, Chongqing, China.
The developments of society and the living standards have greatly facilitated people's travel. Thus, how to choose a tourism strategy becomes an important problem in many tourist routes. Aiming at the tourist route optimization problem of nine cities in China, this paper first collects the track mileage and the actual high-speed train or train fares between every two cities. Second, the optimal distance and cost of the travel are solved by the ant colony algorithm (ACA) based on the datum collected, respectively. Finally, travel evaluation index of combination the travel time with the fare are computed. Then the values of the travel index matrix are regarded as the weights in the weighed graph of the traveling salesman problem. Similarly, the ACA is implemented to solve the optimal tourism route with travel time and fare. On the whole, reasonable and optimal travel route and booking schemes are proposed for this practical tourism problem and provided for the traveler to choose a suitable travel route.
Ant Colony Algorithm, Shortest Distance, Least Cost, Travel Evaluation Index
[01] Zhang, L. B., Zhou, C. G., Ma, M., et al. (2004). Solving Multi-objective Optimization Problem Based on Particle Swarm Optimization. Computer Research and development. 41 (7): 1286-1291.
[02] Dorigo, M. (1992). Optimization, Learning and Natural Algorithms, PhD thesis, Politecnico di Milano, Italy.
[03] Behdanib, C. (2014). An Integer-programming base Approach to the Close-Enough Traveling Salesman Problem. Informs Journal Computing, 26 (3): 415-432.
[04] Quanhy, J. (2003). An Evolutionary Algorithm for TSP, Hunan Normal University (Natural Science Edition). 199 (2): 28-34.
[05] Wang, H. L. (2005). Hybrid Immune Algorithm and its Application in Solving TSP. Xi' an: Northwest University.
[06] Dorigo, M., Birattari, M. and Stiitzle, T. (2006). Ant colony optimization: Artificial Ants as a Computational Intelligence Technique. IEEE Computational Intelligence Magazine. 1 (4): 61-72.
[07] Zong, D. C., Wang, K. K. and Ding, Y. (2004). A review of the ant colony algorithm for solving travel problems. Computer and Mathematics. 11:2004-2013.
[08] Li, L. and Gong, S. H. (2003). Dynamic ant colony optimsation for TSP. Int J Adv Manuf Technol. 22: 528-533.
[09] Yu, M. M. (2009). Improved genetic algorithm based on ant colony algorithm. Journal of Anhui University of Science and Technology, 29 (3): 58-63.
[10] Cheng, C. H., Gunasekaran, A. and Woo, K. H. (2017). A bi-tour ant colony optimisation framework for vertical partitions. Int. J. Ind. Syst. Eng. 7 (3): 341-356.
[11] Cui, Y., Geng, Z., Zhu, Q. and Han, Y. (2017). Review: Multi-objective optimization methods and application in energy saving. Energy, 125: 681-704.
[12] Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. Systems man and cybernetics, 26 (1), 29-41.
[13] Dorigo, M., & Blum, C. (2005). Ant colony optimization theory: a survey. Theoretical Computer Science, 344 (2), 243-278.
[14] Blum, C. (2005). Ant colony optimization: Introduction and recent trends. Physics of Life Reviews, 2 (4), 353-373.
[15] Abdulkader, M. M., Gajpal, Y., & Elmekkawy, T. Y. (2015). Hybridized ant colony algorithm for the Multi Compartment Vehicle Routing Problem. Applied Soft Computing, 196-203.
[16] Yang, J., Shi, X., Marchese, M., & Liang, Y. (2008). An ant colony optimization method for generalized TSP problem. Progress in Natural Science, 18 (11), 1417-1422.
[17] Padberg, M. W., & Rinaldi, G. (1991). A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems. Siam Review, 33 (1), 60-100.
MA 02210, USA
AIS is an academia-oriented and non-commercial institute aiming at providing users with a way to quickly and easily get the academic and scientific information.
Copyright © 2014 - American Institute of Science except certain content provided by third parties.