Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithm Article Swipe
With the development of computer science, various algorithm models are gradually applied in various fields of life. In order to study the application of the multi-objective differential evolution algorithm in the field of sports transportation. Based on the improvement of multi-objective differential evolution algorithm, this paper proposes the training model of PE education, and compares the prediction results and the actual results. The specific conclusions are as follows: (1) MODE algorithm is better to other algorithms in convergence speed and accuracy; MODE algorithm can not only reach the optimal particle position quickly, but also fluctuate around the best point.(2) AMODE-MPS has great potential for dealing with complex and multiple objectives.(3) There are significant differences between the prediction performance of the proposed algorithm model and the statistical performance, in which the statistical performance is significantly higher than the predicted performance.(4) The proposed model can basically meet the prediction requirements. Although there are some differences between the prediction results and the actual results, this is because the statistical process is affected by the weather, physical condition and other factors. The results show that the PE training model has good results in practice, so this paper can provide reference for the improvement of PE teaching model.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.sasc.2025.200200
- OA Status
- diamond
- Cited By
- 2
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407430754
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- OpenAlex ID
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https://openalex.org/W4407430754Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.sasc.2025.200200Digital Object Identifier
- Title
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Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-02-13Full publication date if available
- Authors
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M. WuList of authors in order
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https://doi.org/10.1016/j.sasc.2025.200200Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.sasc.2025.200200Direct OA link when available
- Concepts
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Training (meteorology), Computer science, Evolutionary algorithm, Differential (mechanical device), Differential evolution, Algorithm, Artificial intelligence, Machine learning, Engineering, Physics, Aerospace engineering, MeteorologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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