Passenger Flow Prediction of Tourist Attractions by Integrating Differential Evolution and GWO

To further improve the experience of visiting tourist attractions and promote their long-term healthy development, this study analyzes the short-term passenger flow prediction of tourist attractions. The traditional long short-term memory network is selected as the basis of the prediction framework, differential evolution algorithm and grey wolf optimization algorithm are introduced to automatically optimize its hyperparameters, and a passenger flow prediction model is constructed based on intelligent optimization and deep learning. The experiment outcomes denote that the differential evolution improvement strategy designed in the study is beneficial for improving the global optimization of the grey wolf evolutionary algorithm. The average optimization values of different test functions are closest to the global minimum, effectively improving the population fitness. In the parameter optimization, the maximum value of hyper volume can reach 0.91. The minimum value of the inverse generation distance converges to 0.09, and the quality of the Pareto front solution is relatively high. The Spacing and Spread values are both above 0.8, indicating better diversity in the solution set. The improved prediction model has the lowest values in terms of average absolute percentage error, root mean square error, and mean absolute error. The maximum R-squared value can reach 0.945, indicating good prediction accuracy and goodness of fit. This study enriches the theoretical basis for optimizing and improving traditional time series models, improves the accuracy of predicting tourist flow in tourist attractions, and helps promote the healthy development of the tourism industry.