The Optimal Travel Path Model Based on Greedy and Genetic Algorithm

With the implementation of China’s visa-free transit policy, traveling to China is gradually being welcomed by foreign tourists. In order to comprehensively explore the tourist paths and plans of Chinese cities, this paper analyzes and evaluates the best tourist cities based on 352 cities in China from the aspects of scenic spot rating, tickets, and comprehensive factors of cities, and proposes reasonable and efficient tourism plans according to different tourists' experience requirements. This paper first determines the number and distribution of scenic spots by integrating and sorting the data given. Then, the top 15 cities with the highest rating of scenic spots are selected initially, and 15 different indicators are selected from the five aspects of urban development level, environmental protection, cultural heritage, convenient transportation and food as the basis for judging the level of attracting foreigners. The main method is to use factor analysis (FA) and principal component analysis to evaluate the key indicators that have an important effect on tourists. Then, the comprehensive evaluation score of the indicators obtained from the analysis is carried out, and the multiple aspects are weighted. Finally, 50 cities starting from Guangzhou are coded, and then a combination of genetic algorithm model and greedy algorithm is used to obtain multiple local optimal solutions by greedy algorithm, which are then used as the initial population of genetic algorithm to speed up the iteration speed. The optimal path and the best play plan are simulated and analyzed. At the same time, this paper respectively takes the mountain scenery with the least total cost and the most visited as the constraint conditions, and solves the best specific tour route. Thus a complete path model is constructed.