Application of improved GA in optimizing rural tourism routes - Yin Longjie; Li Muzi (Nonlinear Engineering)
With the growth of the economy, tourism has gradually become a consumer trend in people’s lives. However, the traditional optimization method of rural tourism routes cannot optimize the routes effectively and achieve the best results. The study introduces a genetic algorithm (GA), combines it with an ant colony optimization (ACO) algorithm, and proposes an improved GA to optimize rural tourism routes. The degree of adaptation is set by collecting historical rural tourism route data, initializing the data population, and performing variant crossover operations. The ACO algorithm is used to converge the route points and shift the route according to the action probability. The experimental results show that the proposed method of the study has a minimum mean absolute error of 0.784 and a maximum mean absolute error of 0.801 in optimizing rural tourism routes, which is more accurate compared to the traditional GA and ACO algorithms. Compared with the methods proposed by other scholars, the maximum error rate of solving the proposed method of the study is only 0.56%. The results show that the improved genetic method proposed by the study can effectively enhance the effect of rural tourism route optimization, providing new ideas and perspectives for the tourism industry.