Abstract
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.
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Acknowledgments
I. Fister Jr. acknowledges the financial support from the Slovenian Research Agency (Research Core Founding No. P2-0057). I. Fister acknowledges the financial support from the Slovenian Research Agency (Research Core Founding No. P2-0041). A. Iglesias and A. Galvez would like to thank the financial support from the projects TIN2017-89275-R (AEI/FEDER, UE) and PDE-GIR (H2020, MSCA program, ref. 778035).
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Fister, I., Fister, D., Iglesias, A., Galvez, A., Rauter, S., Fister, I. (2019). Population-Based Metaheuristics for Planning Interval Training Sessions in Mountain Biking. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11655. Springer, Cham. https://doi.org/10.1007/978-3-030-26369-0_7
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DOI: https://doi.org/10.1007/978-3-030-26369-0_7
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