Metamodelling and search using space exploration and unimodal region elimination for design optimization

Younis, Adel; Dong, Zuomin

doi:10.1080/03052150903325540

Cited by 34 publications

(7 citation statements)

References 26 publications

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“…To handle simulation-based problems efficiently, one strategy is to reduce the design space so that optimization effort is relieved (Shin and Grandhi 2001;Wang and Simpson 2004;Younis and Dong 2010a). However, this strategy may miss the global optimum or the important sub-space.…”

Section: Introductionmentioning

confidence: 99%

A global optimization algorithm for simulation-based problems via the extended DIRECT scheme

Liu

Wang

et al. 2014

Engineering Optimization

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This article presents a global optimization algorithm via the extension of the DIviding RECTangles (DIRECT) scheme to handle problems with computationally expensive simulations efficiently. The new optimization strategy improves the regular partition scheme of DIRECT to a flexible irregular partition scheme in order to utilize information from irregular points. The metamodelling technique is introduced to work with the flexible partition scheme to speed up the convergence, which is meaningful for simulation-based problems. Comparative results on eight representative benchmark problems and an engineering application with some existing global optimization algorithms indicate that the proposed global optimization strategy is promising for simulation-based problems in terms of efficiency and accuracy.

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Section: Introductionmentioning

confidence: 99%

A global optimization algorithm for simulation-based problems via the extended DIRECT scheme

Liu

Wang

et al. 2014

Engineering Optimization

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“…Jin et al, 2002). Interactive approaches combining the explicit function and the meta-model can be employed where local meta-models are estimated using progressively generated evaluations of the explicit function (e.g., Younis and Dong, 2010;Emmerich et al, 2006;Zhou et al, 2007;Büche et al, 2004).…”

Section: Model Validation Within Evolutionary Optimization Methodsmentioning

confidence: 99%

Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation

Bischl

Mersmann

Trautmann

et al. 2012

Evolutionary Computation

152

118

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Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and are systematically discussed with respect to possible pitfalls, shortcomings, and specific features. A survey of meta-modeling techniques within evolutionary optimization is provided. In addition, practical examples illustrating some of the pitfalls associated with model selection and performance assessment are presented. Finally, recommendations are given for choosing a model validation technique for a particular setting.

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“…By avoiding redundant searches, the approach identifies the global optimum with a reduced number of objective function evaluations and computation effort. The authors also recently introduced another global optimization algorithm; the algorithm is based on space filling design experiments, region exploration and Kriging metamodel, namely the Space Exploration and Region Elimination (SEUMRE) method [31].…”

Section: Related Workmentioning

confidence: 99%

Global Optimization Using Mixed Surrogates and Space Elimination in Computationally Intensive Engineering Designs

Younis

Dong

2012

International Journal for Computational Methods in Engineering

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Surrogate-based modeling is an effective search method for global design optimization over well-defined areas using complex and computationally intensive analysis and simulation tools. However, indentifying the appreciate surrogate models and their suitable areas remains a challenge that requires extensive human intervention. In this work, a new global optimization algorithm, namely Mixed Surrogate and Space Elimination (MSSE) method, is introduced. Representative surrogate models, including Quadratic Response Surface, Radial Basis function, and Kriging, are mixed with different weight ratios to form an adaptive metamodel with best tested performance. The approach divides the field of interest into several unimodal regions; identifies and ranks the regions that likely contain the global minimum; fits the weighted surrogate models over each promising region using additional design experiment data points from Latin Hypercube Designs and adjusts the weights according to the performance of each model; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The proposed algorithm was tested using several benchmark problems for global optimization and compared with several widely used space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the proposed method an excellent tool for computationally intensive global design optimization problems.

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Metamodelling and search using space exploration and unimodal region elimination for design optimization

Cited by 34 publications

References 26 publications

A global optimization algorithm for simulation-based problems via the extended DIRECT scheme

A global optimization algorithm for simulation-based problems via the extended DIRECT scheme

Resampling Methods for Meta-Model Validation with Recommendations for Evolutionary Computation

Global Optimization Using Mixed Surrogates and Space Elimination in Computationally Intensive Engineering Designs

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