Scholarlink

With the development of tourism, how to effectively balance the multiple factors such as tourist flow, economic benefits, ecological environmental protection and infrastructure has become an important issue that needs to be further explored. In this paper, we have constructed an optimization model aiming at maximizing tourism revenue taking into account the number of tourists, infra-structure pressure and environmental changes. After that, we take Juneau, Alaska as an example. We input the data of each factor collected into the corresponding model, thereby obtaining the fitting curves of each factor. Meanwhile, we also take into account the specific circumstances of the local area and impose constraints on various factors. Eventually, we obtain an optimization function that can be solved. Due to the complexity of this function, using the conventional methods to solve it would take a considerable amount of time. Therefore, we adopt a group intelligence optimization algorithm- Multi-Strategy Improved Grey Wolf Optimization Algorithm to solve it. The optimal number of tourists is 1,205,000. At this point, the tourism revenue is $358,135,300.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Human-Driven and Intelligent Optimization: Collaborative Governance and Model Construction in Sustainable Tourism Development

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Lu Sun,
  • Xiaoke Liu,
  • Hanen Jiang,
  • Taixin Chen,
  • Wenna Tan,
  • Jiayi Zhu

摘要

With the development of tourism, how to effectively balance the multiple factors such as tourist flow, economic benefits, ecological environmental protection and infrastructure has become an important issue that needs to be further explored. In this paper, we have constructed an optimization model aiming at maximizing tourism revenue taking into account the number of tourists, infra-structure pressure and environmental changes. After that, we take Juneau, Alaska as an example. We input the data of each factor collected into the corresponding model, thereby obtaining the fitting curves of each factor. Meanwhile, we also take into account the specific circumstances of the local area and impose constraints on various factors. Eventually, we obtain an optimization function that can be solved. Due to the complexity of this function, using the conventional methods to solve it would take a considerable amount of time. Therefore, we adopt a group intelligence optimization algorithm- Multi-Strategy Improved Grey Wolf Optimization Algorithm to solve it. The optimal number of tourists is 1,205,000. At this point, the tourism revenue is $358,135,300.