Multi-modal Optimization Model Based on APF-NSGAII Algorithm

This study innovatively constructs an APF evaluation model integrating analytic hierarchy process (AHP), particle swarm optimization (PSO) and fuzzy comprehensive evaluation (FCE), and integrates non-dominated sorting genetic algorithm II (NSGA-II) to construct a multi-objective intelligent decision-making framework. The index weight optimization is realized by algorithm coupling, and the evaluation system of tourism resource carrying capacity including 34 evaluation dimensions is established by combining fuzzy comprehensive evaluation. NSGA-II is used to solve multi-objective collaborative optimization, which breaks through the limitations of traditional single-objective optimization. Taking Hainan Province as a case to verify the model, it is found that the overload degree of Sanya and Haikou core scenic spots is 1.04 and 0.98 respectively, and it is necessary to implement the tourist diversion strategy. The surrounding cities such as Wenchang (0.82) and Wuzhishan (0.79) have the potential to increase the carrying capacity of tourists. Through the optimization model of fiscal expenditure structure, under the standard of 140yuan per capita tourism tax and fee, the optimal ratios of transportation infrastructure, environmental governance and community construction are 16 %, 18 % and 6.8 % respectively. The evaluation-optimization-regulation full-link intelligent decision support system constructed in this study provides a quantifiable algorithm-driven solution for the sustainable development of tourism.