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

2026-01-29

AI Summary

This work addresses the challenge of obtaining diverse, high-performance feasible solutions in high-dimensional, heavily constrained optical system design, where conventional optimization methods often converge to a single local optimum. The authors propose LDG-EA, a two-stage framework: first, the design space is partitioned using a behavior descriptor based on curvature signs and material indices, with a probabilistic model guiding global exploration; second, within each partition, an evolutionary algorithm integrating covariance matrix adaptation and a Hill-Valley mechanism efficiently locates multiple local optima. This approach pioneers the use of behavior descriptors in multimodal optical design, substantially enhancing solution diversity and coverage. Applied to a six-element Double-Gauss configuration, LDG-EA generates an average of 14,500 candidate solutions spanning 636 unique descriptors—nearly an order of magnitude more than CMA-ES—within under one hour, achieving solution quality comparable to reference designs.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: Learning & Optimization for CV
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

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.

Problem

• optical system design
• multimodal optimization
• lens design
• high-dimensional constrained optimization
• diverse solution discovery

Innovation

• Lens Descriptor
• Multimodal Optimization
• Evolutionary Algorithm
• Glass Selection
• Hill-Valley EA