# Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction

arXiv: https://arxiv.org/abs/1809.06674 · HTML: https://mlsanity.com/paper/1809.06674/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2018.

## Built on by (most cited first, 8 of 8)

- [A neural operator-based surrogate solver for free-form electromagnetic inverse design](https://mlsanity.com/paper/2302.01934/) (2023)
- [Bayesian optimization with improved scalability and derivative information for efficient design of nanophotonic structures](https://mlsanity.com/paper/2101.02972/) (2021)
- [Bayesian Target-Vector Optimization for Efficient Parameter Reconstruction](https://mlsanity.com/paper/2202.11559/) (2022)
- [A Framework for Discovering Optimal Solutions in Photonic Inverse Design](https://mlsanity.com/paper/2106.08419/) (2021)
- [Recent advances in Bayesian optimization with applications to parameter reconstruction in optical nano-metrology](https://mlsanity.com/paper/2107.05499/) (2021)
- [Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions](https://mlsanity.com/paper/2305.02663/) (2023)
- [Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonics](https://mlsanity.com/paper/2602.18148/) (2026)
- [When Every Simulation Counts: Value-Based Reinforcement Learning for Accelerated Photonics Inverse Design](https://mlsanity.com/paper/2607.23469/) (2026)

## Abstract (arXiv)

Numerical optimization is an important tool in the field of computational physics in general and in nano-optics in specific. It has attracted attention with the increase in complexity of structures that can be realized with nowadays nano-fabrication technologies for which a rational design is no longer feasible. Also, numerical resources are available to enable the computational photonic material design and to identify structures that meet predefined optical properties for specific applications. However, the optimization objective function is in general non-convex and its computation remains resource demanding such that the right choice for the optimization method is crucial to obtain excellent results. Here, we benchmark five global optimization methods for three typical nano-optical optimization problems: removeddownhill simplex optimization, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, particle swarm optimization, differential evolution, and Bayesian optimization addedparticle swarm optimization, differential evolution, and Bayesian optimization as well as multi-start versions of downhill simplex optimization and the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm. In the shown examples from the field of shape optimization and parameter reconstruction, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.