Affiliations: [a] Harbin Institute of Technology, Harbin, Heilongjiang 150001, China | [b] Guilin Tourism University, Guilin, Guangxi 541006, China | [c] School of Management Harbin Institute of Technology, Harbin, Heilongjiang 150006, China
Correspondence:
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Corresponding author: Xiaojuan Yuan, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China. E-mail: xiaojuanyhit@yeah.net.
Abstract: With the development of China’s economy, living standards of people have been improving day by day, and tourism has become a way for most people to spend their leisure time. In order to make travel more efficient and fun, travel routes need to be properly planned before traveling. There are many intelligent algorithms that can be used to optimize the routes. The Benchmark27, Att48 and kroA100 travel simulation problems were solved by the ant colony algorithm and the improved ant colony algorithm on the Matlab simulation platform. The optimal path search capabilities of two kinds of algorithms were compared, and the path diagram was drawn. The results showed that the optimal path and average path length of the improved ant algorithm of the improved ant colony algorithm were smaller than those of the ant colony algorithm in solving the three simulation problems, and moreover the improved ant colony algorithm had higher iterative convergence speed and smaller optimal solution than the ant colony algorithm.
Keywords: The improved ant colony algorithm, tourism routes, multiple constraint, the optimal solution
