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
Citations
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Multidisciplinary Design Optimization of Sound Radiation from Underwater Double Cylindrical Shell Structure
TL;DR: In this paper, the authors used the Latin hypercube sampling method to select sampling points, and considered three types of surrogate models, polynomial response surface approximation, Kriging and radial basis neural network, to approximate the acoustic radiation of a double cylindrical shell structure.
Proceedings ArticleDOI
Non-Myopic Multifidelity Method for Multi-regime Constrained Aerodynamic Optimization
Francesco Di Fiore,Laura Mainini +1 more
TL;DR: In this article , a non-myopic multifidelity Bayesian framework aimed at including expensive high-fidelity computational fluid dynamics simulations for the optimization of the aerodynamic design is proposed, which comes with a two-step lookahead policy to maximize the improvement of the solution quality considering the rewards of future steps, and an active learning scheme informed by the fluid dynamic regime and the information extracted from data.
Proceedings ArticleDOI
Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
TL;DR: In this article , a self-adaptive multi-surrogate assisted efficient global optimization algorithm (SAMA-DiEGO) is proposed and further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal problems against several state-of-the-art surrogate assisted optimization algorithms.
Book ChapterDOI
A systems engineering-driven decomposition approach for large-scale industrial decision-making processes
TL;DR: In this paper, a framework is derived from the typically design-centric Systems Engineering methodology, adapted toward manufacturing and its digital technologies, whose behavior varies with time, through multiple and complex interfaces, affected by both controllable inputs and uncertainties from various sources.
References
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