| Title |
Multi-Objective Multi-Generation Gaussian Process Optimizer |
| Authors |
- X. Huang, M. Song, Z. Zhangpresenter
SLAC, Menlo Park, California, USA
|
| Abstract |
We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.
|
| Funding |
DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR. |
| Paper |
download WEPAB304.PDF [1.406 MB / 4 pages] |
| Export |
download ※ BibTeX
※ LaTeX
※ Text/Word
※ RIS
※ EndNote |
| Conference |
IPAC2021 |
| Series |
International Particle Accelerator Conference (12th) |
| Location |
Campinas, SP, Brazil |
| Date |
24-28 May 2021 |
| Publisher |
JACoW Publishing, Geneva, Switzerland |
| Editorial Board |
Liu Lin (LNLS, Campinas, Brazil); John M. Byrd (ANL, Lemont, IL, USA); Regis Neuenschwander (LNLS, Campinas, Brazil); Renan Picoreti (LNLS, Campinas, Brazil); Volker R. W. Schaa (GSI, Darmstadt, Germany) |
| Online ISBN |
978-3-95450-214-1 |
| Online ISSN |
2673-5490 |
| Received |
17 May 2021 |
| Accepted |
12 July 2021 |
| Issue Date |
28 August 2021 |
| DOI |
doi:10.18429/JACoW-IPAC2021-WEPAB304 |
| Pages |
3383-3386 |
| Copyright |
 Published by JACoW Publishing under the terms of the Creative Commons Attribution 3.0 International license. Any further distribution of this work must maintain attribution to the author(s), the published article's title, publisher, and DOI. |
|
