SVD-guided hybrid optimization framework for ab initio aspheric optical design

Automatic lens design remains challenging under multiple competing constraints. For complex aspheric systems, the increasing number of optimization variables and strong parameter coupling often make the optimization process susceptible to local minima. To address this problem, we propose an automatic aspheric design framework centered on feasibility-aware exploration. The core component is an SVD-guided perturbation strategy that explores low-sensitivity aspheric directions while bounding the induced sag variation, thereby improving local-minimum escape and reducing the risk of ray-tracing failures. To further enhance global exploration, a structured sparse differential evolution (SSDE) strategy is introduced to search within structural and aspheric parameter subspaces. The framework is implemented with approximately orthogonal Qcon surface parameterization and continuous material optimization in a balanced latent space. Experiments on a Cooke triplet spherical system, a four-element aspheric system, and two challenging six-element aspheric systems demonstrate the effectiveness of the proposed method. Compared with patent designs and existing automatic design methods, our framework achieves superior RMS spot-size performance under stricter manufacturability constraints. These results suggest that the proposed framework offers a robust and scalable solution for automatic optical system design, with potential for extension to more challenging and diverse design scenarios.