PSO is widely used to solve complex optimization problems in practical problems in the fields of engineering, science and management. Designing new strategies to deal with the performance and efficiency bottlenecks of the algorithm is a research hotspot in this field. In order to solve the problem that the original velocity limit strategy of PSO is relatively simple, which may easily lead to slow convergence speed and low performance of the algorithm, this paper proposes a new velocity limit strategy combining iteration and problem dimension. By analyzing the relationship of the algorithm evolutionary state evaluation to iterations and the dimension of problem for particle swarm optimization, a formula was designed to calculate the ESE influenced by the iterations and problem dimension, and calculated the velocity limit on the basis of the ESE, so a particle swarm optimization with velocity limit combining iteration and problem dimension was obtained. Finally, the algorithm was affected by iteration and problem dimensions, adaptive and scalable for solving problems in different dimensions. The results show that the strategy improves the convergence speed and accuracy. Experimental results prove the effectiveness of the algorithm.