New York
LondonBeijing
Matching Community Sports Facilities With Ant Colony Algorithm in National Fitness

Matching Community Sports Facilities With Ant Colony Algorithm in National Fitness

Peng Chen (Anyang University, China) and Tian Tian (Hainan Tropical Ocean University, China)
Copyright: © 2025 |Pages: 20
DOI: 10.4018/IJDST.369653
Article PDF Download
Open access articles are freely available for download

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.
Article Preview
Top

Literature Review

The use of Ant Colony Algorithms (ACAs) in the optimization of community sports facilities has emerged as a significant topic of interest in recent literature. Zhang et al. (2021) proposed a vector pheromone routing method based on the priority Pareto partial order relation to improve evacuation efficiency in sports venues. This approach leverages the principles of ACO to enhance the safety and efficiency of crowd movements during emergency evacuations, ensuring that community sports facilities are safer and more responsive to potential emergencies.

Complete Article List

Search this Journal:
Reset
Volume 16: 1 Issue (2025)
Volume 15: 1 Issue (2024)
Volume 14: 2 Issues (2023)
Volume 13: 8 Issues (2022)
Volume 12: 4 Issues (2021)
Volume 11: 4 Issues (2020)
Volume 10: 4 Issues (2019)
Volume 9: 4 Issues (2018)
Volume 8: 4 Issues (2017)
Volume 7: 4 Issues (2016)
Volume 6: 4 Issues (2015)
Volume 5: 4 Issues (2014)
Volume 4: 4 Issues (2013)
Volume 3: 4 Issues (2012)
Volume 2: 4 Issues (2011)
Volume 1: 4 Issues (2010)
View Complete Journal Contents Listing