Robust efficient global optimisation via adaptive surrogate refinement. Issue 1 (18th November 2019)

• Record Type: Journal Article
• Authors: Le Carrer, Noémie; Moens, David; Faes, Matthias
• Other Names: Eberhardsteiner J. guestEditor.; Schöberl M. guestEditor.
• Is Part Of: Proceedings in applied mathematics and mechanics. Volume 19:Issue 1(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-11-18
• Subjects: Applied mathematics -- Periodicals; Engineering mathematics -- Periodicals; Mathematical physics -- Periodicals; 519
• DOI: 10.1002/pamm.201900474
• Languages: English
• ISSNs: 1617-7061

Abstract

Abstract: Through the combination of a given surrogate model (e.g. Kriging) and a procedure allowing to compute the local optima of a given black‐box function (e.g. tuned Particle swarm optimization), our algorithm produces refined estimates of the global optima of a multidimensional black‐box model. A confidence ellipsoid computed in a Bayesian way is associated to each estimate, and shown to be reliable. The setting is tested on the 2‐dimensional Branin function. Results outperform those given by two state‐of‐the‐art non‐parallelised efficient global optimisation methods.