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

<p>This study begins by assessing the current state of physical education (PE) teaching in higher education institutions. It establishes an optimization index system for PE teaching, utilizing the entropy weighting method to calculate the weights of the indices. These weights are then incorporated into the objective function to define the constraints for optimizing PE teaching, facilitating the construction of a multi-objective optimization model tailored to enhance PE instruction pathways. The model is solved using the NSGA-II optimization algorithm, and an illustrative analysis is conducted. The results indicate a smooth distribution of the superiority and inferiority degrees within the derived PE teaching optimization scheme, characterized by low overall dispersion (mean superiority and inferiority value of 74.2841) and minimal deviation compared to the current status (mean value of 0.09835). This confirms the effectiveness of the proposed model in improving PE teaching. The implications of this study are significant for elevating the standard and quality of PE teaching in colleges and universities, nurturing more professionals for China’s sports industry, and advancing the nation’s sports sector to unprecedented levels.</p>
</abstract>

This study begins by assessing the current state of physical education (PE) teaching in higher education institutions. It establishes an optimization index system for PE teaching, utilizing the entropy weighting method to calculate the weights of the indices. These weights are then incorporated into the objective function to define the constraints for optimizing PE teaching, facilitating the construction of a multi-objective optimization model tailored to enhance PE instruction pathways. The model is solved using the NSGA-II optimization algorithm, and an illustrative analysis is conducted. The results indicate a smooth distribution of the superiority and inferiority degrees within the derived PE teaching optimization scheme, characterized by low overall dispersion (mean superiority and inferiority value of 74.2841) and minimal deviation compared to the current status (mean value of 0.09835). This confirms the effectiveness of the proposed model in improving PE teaching. The implications of this study are significant for elevating the standard and quality of PE teaching in colleges and universities, nurturing more professionals for China’s sports industry, and advancing the nation’s sports sector to unprecedented levels.

Research on Multi-objective Optimization Model of Physical Education Teaching Enhancement Path in Colleges and Universities

Applied Mathematics and Nonlinear Sciences

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

This study begins by assessing the current state of physical education (PE) teaching in higher education institutions. It establishes an optimization index...

Research on Multi-objective Optimization Model of Physical Education Teaching Enhancement Path in Colleges and Universities

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 05. Feb. 2024

Akzeptiert: 20. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1269

Schlüsselwörter
NSGA-II algorithm, Constraints, Entropy weight method, Multi-objective optimization, Physical education teaching

© 2024 Delei Shen, et al., published by Sciendo

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

Research on Multi-objective Optimization Model of Physical Education Teaching Enhancement Path in Colleges and Universities

Online veröffentlicht: 30. Mai 2024

Seitenbereich: -

Eingereicht: 05. Feb. 2024

Akzeptiert: 20. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1269

SchlüsselwörterNSGA-II algorithm, Constraints, Entropy weight method, Multi-objective optimization, Physical education teaching

© 2024 Delei Shen, et al., published by Sciendo

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

Schlüsselwörter
NSGA-II algorithm, Constraints, Entropy weight method, Multi-objective optimization, Physical education teaching