Hybrid and adaptive meta-model-based global optimization

Gu, Jinqing; Li, G. Y.; Dong, Z.

doi:10.1080/0305215x.2011.564768

Cited by 77 publications

(21 citation statements)

References 17 publications

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“…Figure 1 shows the general flowchart of the MBDO strategy. Representative MBDO algorithms include the efficient global optimization algorithm (EGO) (Jones, Schonlau, and Welch 1998), mode-pursuing sampling algorithm (MPS) ), surrogate-model based multi-modal optimization algorithm (Yahyaie and Filizadeh 2011), and the hybrid and adaptive metamodel-based global optimization algorithm (HAM) (Gu, Li, and Dong 2012). More information on the MBDO algorithms can be found in Shan and Wang (2010b).…”

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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“…The proposed algorithm is compared against two metamodel-based algorithms [the hybrid adaptive metamodeling algorithm (HAM) [37] and the constrained optimization using response surfaces (CORS) [35]] and the DIRECT algorithm [5] on the total 53 test functions. All algorithms start with 5n initial points generated by TPLHD except the DIRECT algorithm.…”

Section: Fig 5 Illustration Of a Two-dimensional Gkls Test Functionmentioning

confidence: 99%

“…Note that the constructed metamodels not only contain the information of sampled points but also have the predicted information of unsampled regions. The predicted information is proved to accelerate the optimization in some metamodel-based optimization algorithms [14,[33][34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces

Liu

et al. 2015

J Glob Optim

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This article develops a novel global optimization algorithm using potential Lipschitz constants and response surfaces (PLRS) for computationally expensive black box functions. With the usage of the metamodeling techniques, PLRS proposes a new approximate functionF to describe the lower bounds of the real function f in a compact way, i.e., making the approximate functionF closer to f . By adjusting a parameterK (an estimate of the Lipschitz constant K ),F could approximate f in a fine way to favor local exploitation in some interesting regions;F can also approximate f in a coarse way to favor global exploration over the entire domain. When doing optimization, PLRS cycles through a set of identified potential estimates of the Lipschitz constant to construct the approximate function from fine to coarse. Consequently, the optimization operates at both local and global levels. Comparative studies with several global optimization algorithms on 53 test functions and an engineering application indicate that the proposed algorithm is promising for expensive black box functions.

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“…Therefore, Gu, Li, and Dong (2012) put forward the hybrid and adaptive metamodel (HAM)-based global optimization algorithm, which constructs three representative metamodels concurrently in the optimization process. The HAM method is based on the principle that the points predicted to be promised by three different metamodels are very important and should be sampled and evaluated.…”

Section: Introductionmentioning

confidence: 99%

An adaptive metamodel-based global optimization algorithm for black-box type problems

Jie

Ding

2014

Engineering Optimization

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In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.

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Hybrid and adaptive meta-model-based global optimization

Cited by 77 publications

References 17 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

Global optimization of expensive black box functions using potential Lipschitz constants and response surfaces

An adaptive metamodel-based global optimization algorithm for black-box type problems

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