An Optimal Travel Route Optimization Model Based on Ant Colony Optimization Algorithm

Travel planning is an important part of tourism. Unlike traditional experience journeys, these journeys developed using mathematical modeling techniques are more scientifically reliable. The mathematical model of travel planning problem is based on tourism marketing problem, which can be solved by ant trap algorithm. At the same time, the development of information technology has led to the transformation of tourism travel organization from the traditional experience based design to a higher level. In this work, this paper focuses on the use of advanced ant algorithm to solve the travel booking problem, self-guided route planning problem and intelligent route planning problem. First, this paper proposes an advanced solution to the ACO based travel assignment problem. In order to realize the ant trap algorithm to solve the travel route problem, when solving the travel quota problem, the ant trap algorithm should obtain the optimal solution with high probability, and the solution time of the algorithm should be relatively short. Secondly, this paper improves the path selection probability and pheromone updating rules, locally searches the optimal path, optimizes the algorithm solving process, and determines the logic parameters of the algorithm. Through performance simulation analysis, the algorithm proposed in this work solves the line problem, with high search accuracy and short solution time.