Lens-descriptor guided evolutionary algorithm for optimization of complex optical systems with glass choice

TL;DR

This paper introduces LDG-EA, a novel two-stage evolutionary algorithm that effectively explores multiple local optima in complex optical lens design, producing diverse high-quality solutions within practical time constraints.

Contribution

The paper presents a new multimodal optimization framework for optical lens design that partitions the search space and efficiently finds multiple local minima, outperforming standard methods in diversity.

Findings

LDG-EA generates approximately 14,500 candidate minima, vastly more than baseline methods.

LDG-EA maintains competitive performance with a fine-tuned reference lens.

The approach produces a diverse set of solutions within one hour.

Abstract

Designing high-performance optical lenses entails exploring a high-dimensional, tightly constrained space of surface curvatures, glass choices, element thicknesses, and spacings. In practice, standard optimizers (e.g., gradient-based local search and evolutionary strategies) often converge to a single local optimum, overlooking many comparably good alternatives that matter for downstream engineering decisions. We propose the Lens Descriptor-Guided Evolutionary Algorithm (LDG-EA), a two-stage framework for multimodal lens optimization. LDG-EA first partitions the design space into behavior descriptors defined by curvature-sign patterns and material indices, then learns a probabilistic model over descriptors to allocate evaluations toward promising regions. Within each descriptor, LDG-EA applies the Hill-Valley Evolutionary Algorithm with covariance-matrix self-adaptation to recover multiple distinct local minima, optionally followed by gradient-based refinement. On a 24-variable (18 continuous and 6 integer), six-element Double-Gauss topology, LDG-EA generates on average around 14500 candidate minima spanning 636 unique descriptors, an order of magnitude more than a CMA-ES baseline, while keeping wall-clock time at one hour scale. Although the best LDG-EA design is slightly worse than a fine-tuned reference lens, it remains in the same performance range. Overall, the proposed LDG-EA produces a diverse set of solutions while maintaining competitive quality within practical computational budgets and wall-clock time.