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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Journal ArticleDOI
Advances in surrogate based modeling, feasibility analysis, and optimization: A review
TL;DR: Two of the frequently used surrogates, radial basis functions, and Kriging are tested on a variety of test problems and guidelines for the choice of appropriate surrogate model are discussed.
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Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come?
TL;DR: The extent to which the use of metamodeling techniques inmultidisciplinary design optimization have evolved in the 25 years since the seminal paper on design and analysis of computer experiments is addressed.
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On design optimization for structural crashworthiness and its state of the art
TL;DR: A comprehensive review of the important studies on design optimization for structural crashworthiness and energy absorption is provided in this article, where the authors provide some conclusions and recommendations to enable academia and industry to become more aware of the available capabilities and recent developments in design optimization.
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Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO
TL;DR: This work provides a comprehensive and detailed literature review in terms of significant theoretical contributions, algorithmic developments, software implementations and applications for both MINLP and CDFO, and shows their individual prerequisites, formulations and applicability.
References
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Yoel Tenne,Chi Keong Goh +1 more
TL;DR: This is the first book to introduce the emerging field of computational intelligence in expensive optimization problems and provides both theoretical treatments and real-world insights gained by experience, all contributed by leading researchers in the respective fields.
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Clustered multiple generalized expected improvement: A novel infill sampling criterion for surrogate models
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Expected improvement in efficient global optimization through bootstrapped kriging
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