Research on Tourism Multi-Objective Optimisation Model Based on Genetic Algorithm and Dynamic Planning Algorithm

This research focuses on the development and application of a multi-objective optimisation and evaluation analysis model, which is deeply rooted in the field of machine learning algorithms. The main objective of this model is to solve complex optimisation problems and to use it as a practical application scenario for exploring the sustainable development of tourism. In the implementation of the model, a genetic algorithm (a classical evolutionary algorithm in the field of machine learning) was used to solve the multi-objective optimisation model. Using the city of Juneau, USA as a sample dataset, the genetic algorithm was able to efficiently search the optimal solution space. The determination of the weights in the model is carefully designed, taking multiple influencing factors into account, which is crucial to the accuracy of the model. Through experiments, it was found that reasonable control of the number of tourists (which is one of the key variables in the model) can significantly improve the comprehensive benefits of tourism. In addition, considering the unique characteristics of the tourism industry in the Tibet Autonomous Region of China, a dynamic programming algorithm (a powerful algorithm for solving optimisation problems with overlapping subproblems) was introduced to further optimise the model. By integrating this algorithm, a diversion model is constructed which can effectively adjust the relevant parameters and structure of the original model. This optimised model performs excellently in improving the efficiency of tourism resource allocation in Tibet. It is able to achieve smarter and more efficient management of tourism resources, thus promoting the balanced development of tourism in the region. Extensive experimental validation has been carried out to assess the effectiveness of the model and algorithm. The results show that the proposed model and algorithm are not only capable of handling complex optimisation problems in the tourism industry, but also provide a valuable reference for applying similar machine learning-based optimisation models to solve the sustainable development of tourism in different regions. This study contributes to the theoretical development of machine learning algorithms in optimisation problems and their practical application in real-life scenarios.