Particle swarm optimization(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 evolutionary state evaluation to iterations and the dimension of problem for particle swarm optimization, a formula is designed to calculate the evolutionary state evaluation(ESE) which is influenced by the iterations and problem dimension, and calculates the velocity limit on the basis of the ESE, so a Particle swarm optimization with velocity limit combining iteration and problem dimension is obtained. Finally, the algorithm is 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 show the effectiveness of the algorithm.