Aiming at the problems of industrial robots in torsional vibration optimization, such as the tendency to fall into local optimum, slow optimization speed, and poor optimization effect, this paper proposes an improved method based on the non-dominated Sorting Whale Optimization Algorithm (NSWOA). Firstly, by introducing the non-dominated sorting algorithm to perform Pareto optimization on the three objectives, the exploration ability of the solution space and the distribution performance in multi-objective optimization are significantly enhanced. Secondly, the NSWOA is combined with input shaper technology. Through transfer function transformation, online signal acquisition and offline optimization processing are realized, which avoids the problem that online optimization is prone to exciting system vibration, while offline modeling has low accuracy. The algorithm is verified on the B&R test platform. The results show that compared with PSO, DBO and ACO, the non-dominated sorting whale optimization algorithm based on the input shaper shows significant advantages. The overshoot is reduced by 80.6%, 92.1% and 92.8%, respectively. The system adjustment time is 10.9%, 7.2% and 6.7% of the other three methods, respectively. While significantly suppressing the system torsional vibration, the dynamic performance of the system is only slightly sacrificed, achieving a fast and vibration-free system response. This verifies the rationality and superiority of the algorithm.