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

TL;DR

This paper benchmarks five global optimization methods on nano-optical design problems, demonstrating Bayesian optimization's superior performance in accuracy and efficiency compared to traditional methods.

Contribution

It provides a comparative analysis of optimization techniques for nano-optics, highlighting Bayesian optimization's advantages in speed and results quality.

Findings

Bayesian optimization outperforms other methods in accuracy.

Bayesian optimization requires less computational time.

Traditional methods are less efficient for complex nano-optical problems.

Abstract

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: \removed{downhill simplex optimization, the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm, particle swarm optimization, differential evolution, and Bayesian optimization} \added{particle 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.