Gaussian Process Regression Based Multi-Objective Bayesian Optimization for Power System Design
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Kaifeng Yang & Michael Emmerich & André Deutz & Thomas Bäck, 2019. "Efficient computation of expected hypervolume improvement using box decomposition algorithms," Journal of Global Optimization, Springer, vol. 75(1), pages 3-34, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Jixiang Qing & Ivo Couckuyt & Tom Dhaene, 2023. "A robust multi-objective Bayesian optimization framework considering input uncertainty," Journal of Global Optimization, Springer, vol. 86(3), pages 693-711, July.
- Fuhao Ji & Auralee Edelen & Ryan Roussel & Xiaozhe Shen & Sara Miskovich & Stephen Weathersby & Duan Luo & Mianzhen Mo & Patrick Kramer & Christopher Mayes & Mohamed A. K. Othman & Emilio Nanni & Xiji, 2024. "Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
- Eichfelder, Gabriele & Warnow, Leo, 2023. "Advancements in the computation of enclosures for multi-objective optimization problems," European Journal of Operational Research, Elsevier, vol. 310(1), pages 315-327.
More about this item
Keywords
power system design; multi-objective optimization; gaussian process regression; Bayesian Optimization; expected hypervolume improvement; squared exponential kernel;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12777-:d:935522. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.