Optimization of Pole Vault Parameters Using Particle Swarm Optimization and Genetic Algorithm
Ouadie El Mrimar
, Othmane Bendaou
, Bousselham Samoudi
Abstract: In the context of pole vaulting, performance primarily depends on the physical qualities of the athlete and the characteristics of the pole. Our study simplifies this complexity by modeling the athlete as a point mass and the pole as an elastic structure, called "elastica", based on a mechanical and mathematical model from a previous article by Chau et al. (2019). This approach allows us to explore more deeply the key parameters of pole vaulting and optimize performance effectively. The optimization of parameters, such as the non-dimensional elasticity deflection of the pole ( ) and the non-dimensional initial velocity of the point mass ( ), presents challenges due to the complex interaction between various variables influencing athlete performance. Currently, the focus lies in developing and integrating optimization techniques, particularly Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), to efficiently identify optimal values for parameters, and . In this research, we consider the maximum height ( ) achieved by the point mass in the dynamic modeling of pole vaulting as an objective function , focusing specifically on non-dimensional parameters, including w and . The results of PSO and GA optimization techniques were compared with a state-of-the-art approach to affirm the efficacy of the proposed method. The results obtained show that the optimized parameters, notably the initial velocity ( ) equal to 8.64 m/s and the bending stiffness (EI) equivalent to 2386.8 N.m², led to a significant improvement in maximum jump height of 6.88 m. Therefore, we recommend coaches encourage adjustments in material stiffness based on initial velocity and to implement personalized monitoring to fine-tune parameters according to individual athlete’s progress.
Series on Biomechanics, Vol.37, No.4 (2023), 93-106
Keywords: elasticity; genetic algorithm; optimization; particle swarm optimization; Pole vault
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| Date published: 2023-11-28
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