Planning a sports training program using Adaptive Particle Swarm Optimization with emphasis on physiological...

Area of Science:

• Sports Science
• Computational Optimization

Background:

• Effective sports training plans are crucial for athletic performance but often require costly expert input.
• Poorly designed plans can lead to athlete injury and overtraining.

Purpose of the Study:

• To create a practical cycling training plan that enhances athletic performance.
• To ensure the plan satisfies key physiological constraints: monotony, chronic training load ramp rate, and daily training impulse.
• To utilize Adaptive Particle Swarm Optimization for plan formulation and performance simulation.

Main Methods:

• Adaptive Particle Swarm Optimization with ɛ-constraint methods was employed.
• Physiological constraints including monotony, chronic training load ramp rate, and daily training impulse were integrated.
• Simulations were conducted to predict performance outcomes.

Main Results:

• The developed training plan demonstrated superior athletic performance compared to a British Cycling benchmark.
• The plan successfully satisfied all considered physiological constraints.
• Simulations indicated significant improvements in athletic performance.

Conclusions:

• The optimized cycling training plan offers a practical and effective solution for athletes.
• This approach balances performance enhancement with crucial physiological safety limits.
• Adaptive Particle Swarm Optimization provides a viable method for creating personalized training regimes.