Journal ArticleDOI
Efficient global optimization algorithm assisted by multiple surrogate techniques
TLDR
The multiple surrogate efficient global optimization (MSEGO) algorithm is proposed, which adds several points per optimization cycle with the help of multiple surrogates, and is found that MSEGO works well even with imported uncertainty estimates, delivering better results in a fraction of the optimization cycles needed by EGO.Abstract:
Surrogate-based optimization proceeds in cycles. Each cycle consists of analyzing a number of designs, fitting a surrogate, performing optimization based on the surrogate, and finally analyzing a candidate solution. Algorithms that use the surrogate uncertainty estimator to guide the selection of the next sampling candidate are readily available, e.g., the efficient global optimization (EGO) algorithm. However, adding one single point at a time may not be efficient when the main concern is wall-clock time (rather than number of simulations) and simulations can run in parallel. Also, the need for uncertainty estimates limits EGO-like strategies to surrogates normally implemented with such estimates (e.g., kriging and polynomial response surface). We propose the multiple surrogate efficient global optimization (MSEGO) algorithm, which adds several points per optimization cycle with the help of multiple surrogates. We import uncertainty estimates from one surrogate to another to allow use of surrogates that do not provide them. The approach is tested on three analytic examples for nine basic surrogates including kriging, radial basis neural networks, linear Shepard, and six different instances of support vector regression. We found that MSEGO works well even with imported uncertainty estimates, delivering better results in a fraction of the optimization cycles needed by EGO.read more
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Book ChapterDOI
Global Surrogate Modeling by Neural Network-Based Model Uncertainty
Leifur Leifsson,Jethro Nagawkar,Laurel Barnet,Kenneth M. Bryden,Slawomir Koziel,Anna Pietrenko-Dabrowska +5 more
Journal ArticleDOI
Improving High-dimensional Simulation-driven Optimization
Journal ArticleDOI
MSSRGO: A multimeta-model-based global optimization method using a selection-rank-based infill sampling strategy
TL;DR: In this paper , a multimeta-model-based global optimization method using the selection-rank-based infill sampling strategy (MSSRGO) algorithm is proposed to obtain more precise solutions with satisfactory computing costs for expensive black box problems.
Proceedings ArticleDOI
Surrogate-based aerodynamic shape optimization of contra rotating open rotor
Qihang Wang,Li Zhou,Zhanxue Wang +2 more
TL;DR: In this article , the authors used non-linear programming by quadratic lagrangian (NLPQL) to solve the aerodynamic shape optimization of a contra rotating open rotor.
References
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