Research on the Optimization of Cycling Strategy Based on Dynamic Programming and Genetic Algorithm

This paper aims to optimize the energy distribution strategy in bicycle racing to enhance riding efficiency and reduce race time. The research revolves around three issues: energy allocation for individual cycling, leading and following strategies in group cycling, and cooperation strategies among team members. Firstly, for individual cycling, a dynamic model considering air resistance, friction resistance, and slope impact is established. By applying the principle of energy conservation, the optimal speed distribution scheme is calculated. Secondly, for group cycling, the physical consumption differences between leading and following are analyzed, and the dynamic programming method is used to optimize the time allocation of leading and following. Lastly, a genetic algorithm is employed to optimize the leading time of team members to reduce overall physical consumption and improve team performance. The research results show that reasonable energy distribution and strategy optimization can significantly improve riding efficiency. This study not only provides theoretical guidance for bicycle cycling but also offers effective methods for optimizing similar sports strategies.