Aiming at the disadvantages of slow convergence, low optimization accuracy and easy to fall into local extremum in the sparrow search algorithm for solving large-scale optimization problems, sparrow search algorithm based on the elite reverse learning strategy and firefly strategy(ELFASSA) is proposed. Firstly, the population is initialized using the reverse learning strategy to lay the foundation for global optimization. Then, the firefly perturbation strategy is used to improve the ability of the algorithm to jump out of the local optimum and accelerate the convergence. Finally, after the sparrow position is updated, the elite reverse learning strategy is introduced to obtain the elite solution and dynamic boundary, so that the elite reverse solution can be located in the narrow search space, which is conducive to the convergence of the algorithm. By selecting 10 high-dimensional standard test functions for simulation experiments, its performance is compared with the sparrow search algorithm (SSA) and four advanced improved algorithms, and the effectiveness of the improved strategy is analyzed with three single strategy improved sparrow search algorithms. The simulation results show that the ELFASSA algorithm is obviously superior to other comparison algorithms in convergence speed and solution accuracy.