Tourism Strategy Evaluated by Genetic and Ant Colony Algorithms
[1]
Xiaoyang Zheng, Institute of Liangjiang Artificial Intelligence, Chongqing University of Technology, Chongqing, China.
[2]
Qingsong Liu, College of Science, Chongqing University of Technology, Chongqing, China.
[3]
Yong Fu, College of Science, Chongqing University of Technology, Chongqing, China.
Tourism has experienced continued growth and travel experience has played an important role affecting leisure. Consequently, it is very important to provide optimal tourism strategy for tourists. The main task of this article is that the Genetic algorithm (GA) and Ant Colony Optimization algorithm (ACO) are implemented to choose the best travel route from ten cities in China, respectively. First, the principles of the two optimization algorithms are introduced. Second, two types of travel strategies by air and by car are evaluated by using the GA and ACO, respectively. The different optimal routes are obtained by different algorithm parameters, while the length of these optimal routes is the same 6259 kilometer. Then, these different optimal travel routes can be provided for different tourists. Finally, the influences on the optimal path caused by the parameters of each algorithm are analyzed and compared, respectively. It can be found that the change of the parameters has a great influence on the convergence of the ACO. By comparing the travel time by air with that by car, we offer the tourism strategy and the corresponding the best travel route for tourists.
Tourism Strategy, Genetic Algorithm, Ant Colony Optimization, Optimal Path
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