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Matching Community Sports Facilities With Ant Colony Algorithm in National Fitness

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Listed:
  • Peng Chen

    (Anyang University, China)

  • Tian Tian

    (Hainan Tropical Ocean University, China)

Abstract

This study addresses the challenge of selecting optimal locations for urban sports facilities, leveraging the strengths of the ant colony optimization (ACO) algorithm. An enhanced ACO model is proposed, incorporating population density and distance to sports facilities as critical factors in the objective function. The model employs a unique pheromone updating strategy that reduces search time and improves solution quality. Two updates to the pheromone levels are performed, and the initial pheromone distribution is reset based on path distances. The effectiveness of the model is demonstrated through a case study in Yuhua District, Changsha City, where it successfully identifies prime locations for public sports facilities. This research contributes to the literature on facility siting and urban planning by offering a practical solution for optimizing the distribution of sports infrastructure within cities.

Suggested Citation

  • Peng Chen & Tian Tian, 2025. "Matching Community Sports Facilities With Ant Colony Algorithm in National Fitness," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 16(1), pages 1-20, January.
    • Handle: RePEc:igg:jdst00:v:16:y:2025:i:1:p:1-20
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJDST.369653
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    1. Henry Mintzberg, 1981. "Research notes and communications what is planning anyway?," Strategic Management Journal, Wiley Blackwell, vol. 2(3), pages 319-324, July.
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