Kinematic calibration is one of the main methods to improve the absolute positioning accuracy of industrial robots. Parameter identification is the primary step in determining the calibration effect. However, due to the large number of kinematic parameters of industrial robots and the complex coupling between parameters, the existing parameter identification methods easily fall into local optimal solutions, and it is difficult to obtain accurate kinematic parameters. Therefore, this paper proposes a Particle Swarm Somersault Foraging Zebra Optimization Algorithm (PSSFZOA) for kinematic parameter identification of industrial robots. The proposed PSSFZOA introduces the foraging strategy into the Zebra Optimization Algorithm (ZOA) and linearizes the kinematic error model so that it does not fall into the local optimum in the later stage of optimization, and improves the population diversity in the early stage. In the first stage of the algorithm, the particle swarm strategy is used to provide local optimal particles, which ensures that the algorithm achieves a balance between global search and local search. It has both the ability of deep search and the diversity of breadth search better to find the best in the complex search space. Simulations are carried out to verify the effectiveness of the method. The results show that compared with the existing Grey Wolf Optimization (GWO) and ZOA, the PSSFZOA can avoid falling into local optimum prematurely, accelerate the convergence speed of the algorithm, and improve the absolute positioning accuracy of the industrial robot after parameter identification.