Research on Multi-Objective Optimization Problem of Tourism Based on Genetic Algorithm

This study focuses on the multi-objective optimization problem of complex systems, taking the data related to Juneau tourism in the United States as the research object, aiming at constructing an optimization model that takes into account the benefits of multiple aspects. In the study, firstly, we quantify the social problems by establishing the functional relationship between economic, environmental and social indicators and key factors, and using fuzzy comprehensive evaluation. With the help of hierarchical analysis to determine the weights of each index, based on this, genetic algorithm is used to solve the multi-objective function, to find the optimal solution under the balance of economic, environmental and social interests, to complete the construction of model I, and compared with the actual data analysis. The results show that the optimization strategy can significantly reduce the pressure on the ecological environment and improve the happiness index of the residents at the expense of economic benefits to a certain extent. Subsequently, a tax policy is introduced into Model I, and the tax revenue is used to improve the environment and social conditions, and the genetic algorithm is used again to find a new optimal solution to construct Model II. Comparing the optimal solutions of the two models, it is found that a reasonable tax policy (about 20 US dollars per capita) can effectively improve the carrying capacity of the system and enhance the comprehensive benefits, and the related indexes can be improved by more than 10%. This study provides an effective example for solving multi-objective optimization problems, and has a wide range of potential applications.