Original Harris hawks optimization (HHO) has low convergence accuracy and slow convergence speed and is easy to fall into local optimum. In view of these problems, an improved HHO based on a hybrid strategy (HSHHO) is proposed. Firstly, the Sobol sequence is introduced in the population initialization stage to generate a uniformly distributed population, which enriches the diversity of the population and helps to improve the convergence speed of the algorithm. Secondly, the limit threshold is introduced to make the algorithm perform global exploration when it does not obtain a better value within a certain number of iterations. This can improve the ability of the algorithm to jump out of a locally optimal solution and solve the problem that HHO is prone to fall into a locally optimal solution in late iterations because it only executes the development phase. Finally, a dynamic backward learning mechanism is proposed to improve the algorithm’s convergence accuracy and ability to jump out of the local optimum. The proposed algorithm is tested by nine benchmark functions and six CEC2017 functions and compared with various optimization algorithms and HHO variants. As a result, this study verifies the effectiveness of the proposed strategies and performs Wilcoxon signed rank test, Friedman test, and Quade test. The experimental results show that HSHHO has great performance in terms of convergence speed, optimization accuracy, and statistical tests. Furthermore, the proposed algorithm is applied to the design optimization of welded beams. The results show that HSHHO also has a positive effect on practical engineering optimization problems with constraints.