Research on the Application of Single-Parent Genetic Algorithm Improved by Sine Chaotic Mapping in Parent–Child Travel Path Optimization

This paper proposes a method for recommending parent–child travel destinations and planning travel routes tailored to children of different ages. The method inputs basic information about the attractions (such as ticket prices, geographical locations, opening hours, etc.) into the system database and intelligently recommends suitable attractions based on user-provided data, including the children’s age, travel time, and trip theme. The paper transforms the route planning problem into a Traveling Salesman Problem (TSP) to optimize the travel route further. It presents an improved single-parent genetic algorithm based on sine chaos mapping (SCM-SPGA) to solve and optimize the shortest path for parent–child trips. Experimental results demonstrate that this algorithm has significant advantages in path planning accuracy and efficiency. The method is applied to a tourism dataset of Hainan, providing more personalized and age-appropriate attraction recommendations for tourists planning a parent–child trip to Hainan and optimizing the travel route. The research shows that the proposed method can effectively meet the personalized needs of parent–child travelers, significantly improving the overall travel experience by offering more tailored, efficient, and enjoyable trip-planning solutions.