Journal ArticleDOI
Sequential approximate optimization using variable fidelity response surface approximations
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TLDR
Results show that these types of variable fidelity RSAs can be effectively managed by the trust region model management strategy to drive convergence of MDO problems and the CSSO based sampling strategy was found to be, in general, more efficient in driving the optimization.Abstract:
The dimensionality and complexity of typical multidisciplinary systems hinders the use of formal optimization techniques in application to this class of problems. The use of approximations to represent the system design metrics and constraints has become vital for achieving good performance in many multidisciplinary design optimization (MDO) algorithms. This paper reports recent research efforts on the use of variable fidelity response surface approximations (RSA) to drive the convergence of MDO problems using a trust region model management algorithm. The present study focuses on a comparative study of different response sampling strategies based on design of experiment (DOE) approaches within the disciplines to generate the zero order data to build the RSAs. Two MDO test problems that have complex coupling between disciplines are used to benchmark the performance of each sampling strategy. The results show that these types of variable fidelity RSAs can be effectively managed by the trust region model management strategy to drive convergence of MDO problems. It is observed that the efficiency of the optimization algorithm depends on the sampling strategy used. A comparison of the DOE approaches with those obtained using a optimization based sampling strategy (i.e. concurrent subspace optimization --- CSSO) shows the DOE methodologies to be competitive with the CSSO based sampling methodology in some cases. However, the CSSO based sampling strategy was found to be, in general, more efficient in driving the optimization.read more
Citations
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Journal ArticleDOI
Review of Metamodeling Techniques in Support of Engineering Design Optimization
Gongming Wang,Songqing Shan +1 more
TL;DR: This work reviews the state-of-the-art metamodel-based techniques from a practitioner's perspective according to the role of meetamodeling in supporting design optimization, including model approximation, design space exploration, problem formulation, and solving various types of optimization problems.
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Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models
TL;DR: The proposed method is based on a kriging meta-model that provides a global prediction of the objective values and a measure of prediction uncertainty at every point and has excellent consistency and efficiency in finding global optimal solutions.
Journal ArticleDOI
Approximation methods in multidisciplinary analysis and optimization: a panel discussion
TL;DR: Several common themes arose from the discussion, including differentiating between design of experiments and design and analysis of computer experiments, visualizing experimental results and data from approximation models, capturing uncertainty with approximation methods, and handling problems with large numbers of variables.
Journal ArticleDOI
Sequential kriging optimization using multiple-fidelity evaluations
TL;DR: The proposed extension of the sequential kriging optimization method, surrogate systems are exploited to reduce the total evaluation cost and manifests sensible search patterns, robust performance, and appreciable reduction in total evaluation costs as compared to the original method.
Journal ArticleDOI
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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