基于粒子群算法的公路自行车赛跟骑策略研究

Research on Road Bicycle Race Following Strategy Based on Particle Swarm Optimization

该文以自行车团队计时赛跟骑策略为研究对象。首先,依照骑手身体素质与擅长地段的不同,将骑手分为5类,分别是计时赛专家、短跑运动员、攀岩者、拳击手、多面手;其次,以含6名队员的团队为例,综合考虑赛道和风阻力的影响,建立物理因素和生物因素相结合的混合模型;最后,基于粒子群算法,确定团队中每名队员的最佳功率曲线,由此确定高度适应于赛道和运动员的最佳跟骑方案。

This paper takes cycling team time trial following strategy as the research object. Firstly, riders are classified into five categories(time trial experts, sprinters, climbers, boxers, and versatile riders) according to their physical fitness and specialization in the field;Secondly, a hybrid model combining physical and biological factors is established for a team containing six team members, taking into account the influence of the track and wind resistance;Finally, based on the particle swarm optimization(PSO), the best power curve of each member in the team is determined, so as to determine the best riding scheme that is highly suitable for the track and the athletes.