Improved Genetic Algorithm-Driven Multi-Objective Optimization Model for Sustainable Tourism Development

Abstract

This paper constructs a multi-objective optimization model and focuses on its application in optimizing tourism resource allocation based on genetic algorithm. Firstly, the study determines the weights by establishing a multi-objective function system containing multiple indices, combining the hierarchical analysis method (AHP) and entropy weighting method, and solves the optimal solution by genetic algorithm. Among them, the genetic algorithm adopts Latin hypercubic sampling to generate the initial population and balances the global search and local convergence by dynamically adjusting the replication strategy of indices, and finally obtains the optimal solution under the constraints. Secondly, the study constructs a nonlinear model based on the Sigmoid function, and the analysis is further verified by the Shapley value. Finally, through parameter optimization and constraint design, the practical application value of the model on multiple decision variables is ensured to provide a scientific basis for sustainable tourism policy formulation.