The damped least squares (DLS) method is widely used in optical system design due to its great advantages in the speed of convergence and robustness. However, it is easy to get stuck into local minima, which is probably very close to the starting point, leading to a small search range. The particle swarm optimization (PSO) algorithm is one of the most popular intelligent optimization algorithms which is used to handle problems with a large number of variables benefits from its great randomness. It is helpful to use PSO to deal with situations when getting stuck into local minima. It can jump out of the local minima easily for its randomly searching mode. In this paper, we proposed a novel optimization method for the optical system design which is based on the combination of the improved PSO with DLS to achieve a balance between local and global optimization. By combining the improved PSO with DLS, we can prevent the whole system from falling into the local minima and improve the stability of the algorithm. First, we use the improved PSO to search in the planning area randomly. Second, after finishing the process of SA-PSO, the DLS is added to continue optimizing in a small range to find the final solutions. A machine vision lens has been designed by our proposed optimization algorithm, and the results demonstrates that this algorithm is effective for optical system design.
Jiajun Zhang, Zhaofeng Cen, and Xiaotong Li, "Automated design of machine vision lens based on the combination of particle swarm optimization and damped least squares," Proc. SPIE 11548, Optical Design and Testing X, 1154816 (Presented at SPIE/COS Photonics Asia: October 14, 2020; Published: 10 October 2020); https://doi.org/10.1117/12.2573301.
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