Frontiers in Sport Research, 2022, 4(3); doi: 10.25236/FSR.2022.040308.

## Simulation and Optimization Model of Bicycle Time Trial Based on Genetic Algorithm

Author(s)

Yizhi Zhou, Yuhang Yao, Jiabao Yang

Corresponding Author:
Yizhi Zhou
Affiliation(s)

College of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, 510461, China

### Abstract

We have established a simulation optimization model of bicycle time trial based on genetic algorithm. By using the algorithm, we can not only get the best force of different sections, but also get personalized analysis of different players. These two sets of data can directly help us find the shortest time for a particular rider to finish a particular race. In this paper, we expanded the model biased towards the six-person team time trial, and successfully combined the genetic algorithm to find out the best time duration and the best order for everyone in the team to break the wind. First of all, we built a basic physical simulation model of bicycle individual time trial. The two resistances can be obtained by the basic laws of physics. Then we consider different terrain factors. Based on this, we also built the corresponding topographic map in combination with the actual road conditions. Finally, we consider the physical strength change of the players, and establish a dynamic physical strength change model combined with related papers. After building the basic physical model, we first need to consider the riding ability of a rider. In order to quantitatively describe how much power players should use in different places, we introduce the road cognition coefficient and terrain difficulty degree. Therefore, an optimal power table of a specific rider for a specific track is obtained.

### Keywords

Riding simulation; Cognitive coefficient; Genetic algorithm; Optimal force curve

### Cite This Paper

Yizhi Zhou, Yuhang Yao, Jiabao Yang. Simulation and Optimization Model of Bicycle Time Trial Based on Genetic Algorithm. Frontiers in Sport Research (2022) Vol. 4, Issue 3: 38-41. https://doi.org/10.25236/FSR.2022.040308.

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