Abstract: The traditional Ant colony algorithm and Genetic algorithm used in tourism route planning have some defects, which have a great impact on the accuracy of the algorithm. To solve this problem,Ant colony-genetic algorithm（AC-GA） was proposed. The complementation of the two algorithms could effectively make up for their respective shortcomings and give full play to their greatest advantages in the optimization of tourism routes. Taking 15 scenic spots in a county of Jiangsu Province as an example, the performance of the algorithm was verified by simulation using Matlab software. The results showed that, under the same parameter setting conditions, the number of iterations when using AC-GA fusion algorithm to find the optimal path was far lower than the traditional Ant colony algorithm, and the convergence speed was faster. The length of the optimal route output by the AC-GA fusion algorithm was 2 457.755 3 km shorter than that of the traditional Ant colony algorithm. The average number of iterations in 10 experiments was 51, which was 68.9% lower than the traditional algorithm. The average search time was 9.01s, which was 79.7% lower than the traditional algorithm. To sum up, the performance of AC-GA fusion algorithm was better than that of traditional algorithms, and it was suitable for agricultural tourism route planning research.

Key words: genetic algorithm, ant colony, AC-GA fusion algorithm, agricultural tourism