Research on the development of college students’ sports program based on multi-objective optimization algorithm

被引:0
|
作者
Song S. [1 ]
机构
[1] Chongqing Institute of Foreign Studies, Chongqing
关键词
Chebyshev function; Exercise load; Human motion tracking; Multi-objective optimization; Pareto optimal solution;
D O I
10.2478/amns-2024-0021
中图分类号
学科分类号
摘要
In recent years, the physical fitness of college students has been declining year by year, which has become the focus of national concern. In this paper, firstly, the human body motion tracking based on a multi-objective optimization algorithm is applied in the auxiliary training of college students’ sports to provide data support for the formulation of sports plans, and the Chebyshev function evolution calculation is used to obtain the Pareto optimal solution for sports training. Secondly, the similarity function between the two-dimensional projection of the skeleton and the silhouette of the image is established in the constructed human body model so as to calculate the multi-objective optimization function. The optimized training plan was specifically analyzed after analyzing the sports assessment of college students in X school. The results show that compared with the conventional training plan, the optimized training plan has more sports load intensity indexes distributed between 1.5 and 2.0, indicating that the plan is more scientific and effective. The research presented in this paper can be a valuable resource for the creation of sports programs for college students. © 2023 Shuai Song,
引用
收藏
相关论文
共 50 条
  • [31] An improved multi-objective optimization algorithm based on decomposition
    Wang, Wanliang
    Wang, Zheng
    Li, Guoqing
    Ying, Senliang
    2019 TENTH INTERNATIONAL CONFERENCE ON INTELLIGENT CONTROL AND INFORMATION PROCESSING (ICICIP), 2019, : 327 - 333
  • [32] Research Progress of Dynamic Multi-objective Optimization Evolutionary Algorithm
    Ma Y.-J.
    Chen M.
    Gong Y.
    Cheng S.-S.
    Wang Z.-Y.
    Zidonghua Xuebao/Acta Automatica Sinica, 2020, 46 (11): : 2302 - 2318
  • [33] Research on Stratified Multi-objective Optimization Algorithm in Wireless Networks
    Tu Xionggang
    Chen Jun
    Zhang Changjiang
    INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING, 2015, 8 (04): : 161 - 172
  • [34] CEGA: Research on Improved Multi-objective CE Optimization Algorithm
    Zhao Duo
    Huang Chenxi
    Tang Qichao
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 2463 - 2467
  • [35] The Research of Parallel Multi-objective Particle Swarm Optimization Algorithm
    Wu Jian
    Tang XinHua
    Cao Yong
    2014 5TH IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS), 2014, : 300 - 304
  • [36] Research On Multi-Objective Reactive Power Optimization Based on Modified Particle Swarm Optimization Algorithm
    Wu, Jianhua
    Li, Nan
    He, Lihong
    Yin, Bin
    Guo, Jianhua
    Liu, Yaqiong
    2010 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-5, 2010, : 477 - 480
  • [37] Personalised Multi-Objective Travel Route Recommendation Based on Super Multi-Objective Optimization Algorithm
    Zhang, Xiang-Rong
    Wang, Xue-Ying
    Ebara, Takeshi
    Journal of Network Intelligence, 2024, 9 (03): : 1625 - 1640
  • [38] Multi-objective optimization of reservoir flood dispatch based on multi-objective differential evolution algorithm
    Qin, Hui
    Zhou, Jian-Zhong
    Wang, Guang-Qian
    Zhang, Yong-Chuan
    Shuili Xuebao/Journal of Hydraulic Engineering, 2009, 40 (05): : 513 - 519
  • [39] A Novel Opposition-Based Multi-objective Differential Evolution Algorithm for Multi-objective Optimization
    Peng, Lei
    Wang, Yuanzhen
    Dai, Guangming
    ADVANCES IN COMPUTATION AND INTELLIGENCE, PROCEEDINGS, 2008, 5370 : 162 - +
  • [40] A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization
    Wang P.
    Zhang C.-S.
    Zhang B.
    Wu J.-X.
    Liu T.-T.
    1600, Chinese Institute of Electronics (45): : 2343 - 2347