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The Prediction of Sports Economic Development Prospect in Different Regions by Improved Artificial Bee Colony Algorithm

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  • Lei Liu
  • Guangda Song
  • Lele Qin

Abstract

In order to study the development of the sports economy in different regions and analyze the future development prospect of sports economy, this paper uses the k-clustering method to improve the artificial bee colony algorithm and further improve the clustering degree of the bee colony. Among them, the improved artificial bee colony algorithm reduces the incidence of local extreme and improves the accuracy of calculation by setting the weight and threshold of indicators. MATLAB simulation results show that the prediction accuracy of the improved artificial bee colony algorithm for the development prospect of sports economy is 96–99%, and the calculation time is 0–17 seconds. Therefore, the improved artificial bee colony algorithm can best predict the development of the sports economy in different regions, and its accuracy, periodicity, and calculation time are better than those of the original artificial bee colony algorithm.

Suggested Citation

  • Lei Liu & Guangda Song & Lele Qin, 2022. "The Prediction of Sports Economic Development Prospect in Different Regions by Improved Artificial Bee Colony Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2022, pages 1-8, March.
    • Handle: RePEc:hin:jnddns:7720250
    DOI: 10.1155/2022/7720250
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    Cited by:

    1. Shao, Peng & Liang, Ying & Li, Guangquan & Li, Xing & Yang, Le, 2023. "Birefringence learning: A new global optimization technology model based on birefringence principle in application on artificial bee colony," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 206(C), pages 470-486.

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