Population-Based Metaheuristics for Planning Interval Training Sessions in Mountain Biking

Stochastic population-based nature-inspired metaheuristics have recently revealed that they are a very robust tool for planning sport training sessions in various sports, e.g. running, cycling, triathlon. Most of the existing solutions in literature are focused on planning training sessions for a particular training cycle. Until recently, no special attention was paid to planning interval training sessions, where the high-intensity intervals are followed by low-intensity periods of recovery. This kind of training sessions increases the aerobic capacity of an athlete. In this paper, we propose planning interval training sessions using stochastic population-based nature-inspired metaheuristics. The proposed bat algorithm was tested on an archive of interval training sessions realized by a younger mountain biker, where two different scenarios were taken into account.