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An improved ant colony optimization algorithm based on context for tourism route planning.

Shengbin Liang1, Tongtong Jiao1, Wencai Du2

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This study introduces an improved ant colony optimization algorithm for tourism route planning. The enhanced algorithm prioritizes tourist experience and optimizes routes, reducing travel distance and improving efficiency.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Operations Research

Background:

  • Traditional tourism route planning often prioritizes shortest distance over tourist experience.
  • Scenic spot context, including weather and comfort, significantly influences destination choice.
  • Existing algorithms may fall into local optima, failing to find globally optimal routes.

Purpose of the Study:

  • To develop an improved ant colony optimization algorithm for tourism route planning.
  • To integrate contextual information of scenic spots into the route optimization process.
  • To enhance the tourist experience by optimizing routes beyond mere distance.

Main Methods:

  • An improved ant colony optimization algorithm was proposed.
  • Pheromone update strategy was combined with contextual information (weather, comfort).
  • Sub-path support degree was introduced to avoid local optimization.

Main Results:

  • The optimized tourism routes significantly improved the tourist experience.
  • Route distance was shortened by 20.5% compared to the basic algorithm.
  • Convergence speed increased by 21.2% compared to the basic algorithm.

Conclusions:

  • The improved ant colony optimization algorithm is effective for tourism route planning.
  • Integrating contextual information enhances route suitability for tourists.
  • The algorithm successfully balances route efficiency with improved tourist satisfaction.