Calibration and measurement method of PMD based on the optimization algorithm of swarm intelligent body

Traditional phase measuring deflectometry (PMD) systems encounter challenges related to the stability of calibration parameter optimization, often resulting in insufficient reconstruction accuracy and low computational efficiency. In response to this technical challenge, this paper proposes an optimization method for the two-step calibration of the PMD system based on a swarm-intelligent body optimization algorithm, aiming to significantly improve the reconstruction accuracy and processing efficiency of the system. First, a traditional calibration method is used for the initial calibration of the measurement system. Subsequently, the dung beetle optimization (DBO) algorithm-to our knowledge, a novel swarm intelligence optimization algorithm known for its rapid convergence and global search capabilities-is introduced to construct a multi-objective optimization model with the three-dimensional system reconstruction error as the objective function. By applying the DBO to solve this model, a set of optimal calibration parameters is obtained. Experimental results demonstrate that the DBO algorithm achieves significant improvements in both reconstruction accuracy and stability compared to traditional calibration methods, thereby verifying its effectiveness and practicality in PMD systems.