Abstract: Aiming at the problems that the Sparrow Search Algorithm (SSA) still has premature convergence when solving the optimal solution of the objective function, it is easy to fall into local optimum under multi-peak conditions, and the solution accuracy is insufficient under high-dimensional conditions, a hybrid multi-strategy improved Sparrow Search Algorithm (MISSA) is proposed. Considering that the quality of the initial solution of the algorithm will greatly affect the convergence speed and accuracy of the entire algorithm, an elite reverse learning strategy is introduced to expand the search area of the algorithm and improve the quality and diversity of the initial population; the step size is controlled in stages, in order to improve the solution accuracy of the algorithm. By adding the Circle mapping parameter and cosine factor to the position of the follower, the ergodicity and search ability of the algorithm are improved. The adaptive selection mechanism is used to update the individual position of the sparrow and add Lévy flight to enhance the algorithm optimization and the ability to jump out of local optima. The improved algorithm is compared with Sparrow Search Algorithm and other algorithms in 13 test functions, and the Friedman test is carried out. The experimental comparison results show that the improved sparrow search algorithm can effectively improve the optimization accuracy and convergence speed, and it can be used in high-dimensional problems. It also has high stability.

Key words: sparrow search algorithm, reverse learning, step size control, chaos parameter, self- adaptation