A Nearest Neighbor Secrecy Algorithm for Tourist Routes Based on Mathematical Model

With the improvement of people's living standards, many young people choose to travel on holidays to enjoy their leisure time. Travel route planning is a crucial part of tourism. Compared with the traditional tourist routes designed by experience, it is more scientific and reasonable to formulate through mathematical modeling methods. This paper mainly studies the problem of solving the traveling salesman problem based on the improved neighbor secrecy algorithm, the problem of self-driving travel route planning and the problem of smart travel route planning. This paper proposes a nearest neighbor secrecy algorithm for travel routes based on mathematical models. This paper improves the path selection probability and pheromone update rules, searches for the optimal path locally, optimizes the algorithm solution process, and defines the logic parameters of the algorithm. Through performance simulation analysis, the algorithm proposed in this paper has higher search accuracy and shorter solution time to solve the traveler-seller problem. The final results of the research show that the optimal solution of the nearest neighbor secrecy algorithm is 423.74, the worst solution is 426.37, and the average value is 424.26. The optimal solution of the ant colony algorithm is 427.74, the worst solution is 440.96, and the average value is 438.20. The path optimization distance of the nearest neighbor secrecy algorithm is the shortest, which is better than the path optimization of the other two algorithms.