Personalized Travel Route Recommendation Based on the Improved Shuffled Frog Leaping Algorithm

the public expects to obtain the travel experience with low cost, convenient travel and high comfort, but also has different tour needs such as history, culture, natural landscape and food shopping. Therefore, this paper presents a personalized travel route recommendation method based on a modified Shuffled Frog Leaping Algorithm. First, the optimization model of personalized travel route recommendation is established, and the Shuffled Frog Leaping Algorithm is designed and improved according to the characteristics of the model. By adjusting the controllable accuracy, increasing the screening criteria and enhancing the diversity of the group, reducing the risk of missing the optimal solution, strengthening the local search ability, and improving the solution accuracy of the algorithm. Taking the recommendation problem of personalized tourist routes for a three-day tour in Nanjing as an example, the ticket price, opening time, time and cost, food and accommodation costs of famous scenic spots in Nanjing were collected, and solved based on the improved Shuffled Frog Leaping Algorithm. The experimental results show that the proposed improvement method can obtain a better path solution than the previously improved method, and the recommended route can better meet the individual needs of users.