Zhencheng Li and Li Zhang
Genetic-ant colony optimisation algorithm, digitisation, sustainable, rural tourism, route planning
With the rapid development of tourism, there are many customised methods for rural tourism route planning and management. To save the time for tourists and customise their travel itinerary, an ant colony algorithm based rural tourism route planning and management method is proposed. Firstly, the travelling salesman problem (TSP) is handled in the route planning, and the genetic algorithm is used to solve it, so as to establish the shortest route planning model that meets the constraints. Then, based on this, the updated pheromone is obtained by ant colony algorithm. Finally, the two methods are combined to find the optimal solution path. The results showed that the average fitness value which was closest to the maximum fitness value of the population could be obtained after the study method was iterated for about 100 times. In the comparison of the convergence of Oliver30 Dataset, the study method and other algorithms started to converge after 40th and 30th generation of iteration, respectively. In the convergence comparison of Eil76 Dataset, the study method iteration reached a stable state around the 30th generation. In addition, when the iteration was carried out to the 350th generation, the corresponding loss value of the study method was 0.0714. In the application effect analysis, regardless of the number and distance of villages, the study methods could find better route planning. The above results showed that the study method had faster convergence speed and higher accuracy than other methods, with higher feasibility and validity in rural tourism route planning. ∗ School of Wuxi Vocational College of Science and Technology, Wuxi, Jiangsu, China; e-mail: [email protected] ∗∗ Wuxi City Vocational and Technical College, Wuxi, Jiangsu, China; e-mail: [email protected] Corresponding author: Zhencheng Li
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