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

Author

Listed:
  • Shengbin Liang
  • Tongtong Jiao
  • Wencai Du
  • Shenming Qu

Abstract

To solve the problem of one-sided pursuit of the shortest distance but ignoring the tourist experience in the process of tourism route planning, an improved ant colony optimization algorithm is proposed for tourism route planning. Contextual information of scenic spots significantly effect people’s choice of tourism destination, so the pheromone update strategy is combined with the contextual information such as weather and comfort degree of the scenic spot in the process of searching the global optimal route, so that the pheromone update tends to the path suitable for tourists. At the same time, in order to avoid falling into local optimization, the sub-path support degree is introduced. The experimental results show that the optimized tourism route has greatly improved the tourist experience, the route distance is shortened by 20.5% and the convergence speed is increased by 21.2% compared with the basic algorithm, which proves that the improved algorithm is notably effective.

Suggested Citation

  • Shengbin Liang & Tongtong Jiao & Wencai Du & Shenming Qu, 2021. "An improved ant colony optimization algorithm based on context for tourism route planning," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0257317
    DOI: 10.1371/journal.pone.0257317
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    References listed on IDEAS

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    1. Baohui Shi & Yuexia Zhang, 2021. "A Novel Algorithm to Optimize the Energy Consumption Using IoT and Based on Ant Colony Algorithm," Energies, MDPI, vol. 14(6), pages 1-17, March.
    2. Sven Axsäter, 1990. "Simple Solution Procedures for a Class of Two-Echelon Inventory Problems," Operations Research, INFORMS, vol. 38(1), pages 64-69, February.
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