Hybrid kernel extreme learning machine for evaluation of athletes' competitive ability based on particle swarm optimization

A physical education strategy benefit analysis method based on minimal flux parallel random tree algorithm is proposed. The physical education problem model with the optimization objective functions is put forward. The optimization mathematical model is established and its multi-objective weight adaptive form is presented. Then the parallel random tree algorithm is introduced to solve the physical education strategy benefit model. In order to further improve the performance of the parallel random tree algorithm their parameters are adaptively learned by using the minimal flux. Thus the convergence of the algorithm is improved. Finally the example analysis is performed to verify the effectiveness of the proposed physical education strategy benefit analysis algorithm.