<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Digital technology has been used in a large number of applications in all aspects of life and various fields. In the field of sports, digital training has been increasingly emphasized by frontline coaches, athletes, and sports researchers. In this paper, by building an objective optimization model, combining particle swarm optimization, and optimizing the computation process with the Kriging agent model, we formulate the most suitable training plan for college athletes and improve the scientific level of college athletes’ training. The multi-objective optimization training plan can accurately enhance the athletes’ physical fitness, physical quality, and sports skills, and the IRM and maximal power of bench press and half squat showed an increasing trend after the training. The body composition (body fat) and longitudinal jump (lower limb explosive power) improved to a certain extent before and after the training. There were significant differences in the sports skill evaluation of the 50-meter run and the three-stage continuous frog jump; the p-value was less than 0.05. The p-values were all less than 0.05. The scientific and practicality of the training program for college athletes based on the multi-objective optimization model is confirmed when the value falls below 0.05.</p>
</abstract>

Digital technology has been used in a large number of applications in all aspects of life and various fields. In the field of sports, digital training has been increasingly emphasized by frontline coaches, athletes, and sports researchers. In this paper, by building an objective optimization model, combining particle swarm optimization, and optimizing the computation process with the Kriging agent model, we formulate the most suitable training plan for college athletes and improve the scientific level of college athletes’ training. The multi-objective optimization training plan can accurately enhance the athletes’ physical fitness, physical quality, and sports skills, and the IRM and maximal power of bench press and half squat showed an increasing trend after the training. The body composition (body fat) and longitudinal jump (lower limb explosive power) improved to a certain extent before and after the training. There were significant differences in the sports skill evaluation of the 50-meter run and the three-stage continuous frog jump; the p-value was less than 0.05. The p-values were all less than 0.05. The scientific and practicality of the training program for college athletes based on the multi-objective optimization model is confirmed when the value falls below 0.05.

Multi-objective optimization model-based training program development for athletes in college physical education

Lianyungang Vocational and Technical College

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Digital technology has been used in a large number of applications in all aspects of life and various fields. In the field of sports, digital training has been...

Multi-objective optimization model-based training program development for athletes in college physical education

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 12 mar 2024

Accettato: 09 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1698

Parole chiave
Athlete training, Multi-objective optimization, Particle swarm algorithm, Kriging agent mode

© 2024 Di Guan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Multi-objective optimization model-based training program development for athletes in college physical education

Pubblicato online: 05 lug 2024

Pagine: -

Ricevuto: 12 mar 2024

Accettato: 09 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1698

Parole chiaveAthlete training, Multi-objective optimization, Particle swarm algorithm, Kriging agent mode

© 2024 Di Guan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Athlete training, Multi-objective optimization, Particle swarm algorithm, Kriging agent mode