Metaheuristic Optimization for University Athletes' Training Planning: Balancing Performance and Injury Risk

University athletes hold different mental challenges according their time availability for developing academic and sport activities. Training planning represents a difficult task due to the athlete's mental occupation. However, based on metaheuristic optimization, such as, genetic algorithms, particle swarm optimization, and ant colony optimization is possible to establish better training plans for balancing the performance of competitors and preventing risk, taking into advantage the different seasons during the academic periods. The results show that genetic algorithms produce an opportunity for physical therapists when the making-decision process is carried out for students-athletes, balancing performance and injury risks.