A Study of Motion Performance Based on Genetic Algorithm and Entropy Weighting

As computer algorithms are constantly expanding their application boundaries, it has become a hot topic to explore the innovative applications of genetic algorithms and entropy weighting method in the field of sports science. In this study, the outstanding performance of Chinese swimmer Pan Zhanle in breaking the world record of 100-meter freestyle in the 2024 Paris Olympics is taken as a specific scenario for in-depth algorithm practice and exploration. Firstly, an ideal model is established, and the basic mathematical algorithm is applied to obtain the optimization result of 20.6 seconds for 50m freestyle without considering the physical exertion in the simplified scenario. Then, a practical model is constructed based on genetic algorithm, and a nonlinear planning model is constructed for 100m and 200m freestyle with physical exertion as a constraint. Through the iterative optimization of the genetic algorithm, the optimized results were 46.27 s for 100 m freestyle and 102.55 s for 200 m. The model also clearly explained the influence mechanism of tactics on the results with the help of algorithmic analysis. Finally, the entropy weighting method was used to construct a comprehensive evaluation model, and the algorithm was used to quantify the effects of physical fitness, tactical strategy and psychological quality on the freestyle performance, and the weighting diagrams of the three factors were plotted for different distances of freestyle, and the algorithm was used to realize the quantification of the psychological quality in an innovative way. The model parameters in this study are highly adjustable and can be flexibly adjusted according to different athletes' characteristics. In addition, the concept of explosive force training was innovatively proposed, and the sensitivity analysis of the resistance coefficient k and the athlete mass m was performed by the algorithm, and the results were highly compatible with the actual situation. The research results provide new ideas and methods for the application of computer algorithms in the field of sports.