SWARM ALGORITHMS APPLIED TO FITNESS TESTING OF ATHLETES IN COMPETITION

Introduction: Many countries have increased their investments in human resources and technology for the internal development of competitive sports, leading the world sports scene to increasingly fierce competition. Coaches and research assistants must place importance on feedback tools for frequent training of college athletes, and deep learning algorithms are an important resource to consider. Objective: To develop and validate a swarm algorithm to examine the fitness of athletes during periods of competition. Methods: Based on the swarm intelligence algorithm, the concept, composition, and content of physical exercises were analyzed. Combined with the characteristics of events, the body function files and the comprehensive evaluation system for high-level athletes were established. Results: The insight was obtained that the constant mastery of the most advanced techniques and tactics by athletes is an important feature of modern competitive sports. Physical fitness is not only a valuable asset for athletes but also one of the keys to success in competition. Conclusion: Fitness has become an increasingly prominent issue in competition, and the scientific training of contemporary competitive sports has been increasingly refined.