Research on Evaluation Technology of College Students’ Physical Quality Based on Bee Colony Optimization Algorithm

Abstract

The physical quality of college students is a critical aspect of their overall health and well-being, reflecting their physical strength, endurance, flexibility, speed, and coordination. It serves as an important foundation for academic performance, mental health, and future lifestyle habits. This paper proposes an optimized deep learning framework for evaluating the physical quality of college students by integrating Ant Colony Optimization (ACO) for feature selection and Bee Colony Optimization (BCO) for hyperparameter tuning. A dataset comprising physical indicators such as BMI, 50m sprint, endurance run, sit & reach, and strength test was analyzed for 10 students, with labels classified into four categories: Excellent, Good, Average, and Poor. ACO effectively selected the five most relevant features while eliminating less impactful ones such as pulse rate and height, resulting in a more focused input set. The BCO algorithm was used to optimize key hyperparameters of the deep learning model, including the learning rate (optimized from 0.01 to 0.001), batch size (64 to 32), and dropout rate (0.5 to 0.3), while increasing the number of hidden layers (2 to 3) and neurons per layer (64 to 128). These optimizations led to significant improvements in classification performance, with accuracy increasing from 84.5% to 92.3%, precision from 83.2% to 91.0%, recall from 85.0% to 93.4%, and F1-score from 84.1% to 92.2%. Additionally, training time was reduced from 180 seconds to 125 seconds. Results across 50 training epochs showed consistent metric improvements, confirming the model’s convergence and stability.