Research on Tourism Path Optimisation Based on Improved Ant Colony Algorithm

This paper investigates the challenges of path optimization in the field of tourism planning and proposes an innovative diversified objective function optimization method. By comprehensively considering the tourists' preference and the distance between attractions, the two dimensions of data are combined into the same objective function for optimization. Based on the basic ant colony algorithm, the global search capability and algorithm stability are improved by dynamically adjusting the heuristic factors. Experiments prove the effectiveness of the improved algorithm. This study is of great significance in solving the optimization problem of tourism routes and proposes the direction of diversified objective functions to maximize tourists' experience and increasing constraint rules to fit the actual development goals.