Evolutionary intelligent physical education mechanism in smart cities

The rapid evolution of smart cities has created unprecedented opportunities to revolutionise physical education through advanced technologies and data-driven optimisation. This paper proposes an Evolutionary Intelligent Physical Education (EIPE) algorithm that employs surrogate-assisted evolutionary computation to optimise physical education programs in smart cities efficiently. EIPE incorporates diversity-based candidate generation, intelligent selection strategies and adaptive surrogate model management to balance exploration and exploitation in the vast search space of program configurations. The algorithm's effectiveness is evaluated on benchmark problems specifically adapted to reflect real-world physical education scenarios. Experimental results demonstrate EIPE's superior performance to state-of-the-art approaches, achieving 27.8% better convergence and 35.9% improved solution diversity. The algorithm's ability to efficiently handle multiple competing objectives while maintaining solution diversity makes it particularly suitable for optimising physical education programs in modern smart cities, where program adaptability and resource efficiency are crucial for promoting public health and well-being.