Benchmark of Global Optimization Approaches for Nano-optical Shape Optimization and Parameter Reconstruction
This blog post is based on the publication P.-I. Schneider, et al. Benchmarking five global optimization approaches for nano-optical shape optimization and parameter reconstruction. ACS Photonics 6, 2726 (2019).
DOI: 10.1021/acsphotonics.9b00706
Several global optimization methods for three typical nano-optical optimization problems are benchmarked: particle swarm optimization, differential evolution, and Bayesian optimization as well as multistart versions of downhill simplex optimization and the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm. In the shown examples, Bayesian optimization, mainly known from machine learning applications, obtains significantly better results in a fraction of the run times of the other optimization methods.
This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in ACS Photonics, copyright © American Chemical Society after peer review. To access the final edited and published work see https://doi.org/10.1021/acsphotonics.9b00706.