Mode-pursuing sampling method for global optimization on expensive black-box functions

By Liqun Wang, S. Shan, G. G. Wang. 2004

220 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/baaa4cfc6673c3875ad63cd617bdbe87694528af

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Yew-Soon Ong
2. Haitao Liu
3. Jianfei Cai
4. G. G. Wang
5. S. Shan
6. Saman Razavi
7. Donald H. Burn
8. Bryan Tolson

In Statistical and Nonlinear Physics

1. S. Shan
2. G. G. Wang
3. Yew-Soon Ong
4. Jianfei Cai
5. Haitao Liu
6. Donald H. Burn
7. Bryan Tolson
8. Saman Razavi

In Statistics and Probability

1. G. G. Wang
2. S. Shan
3. Yew-Soon Ong
4. Jianfei Cai
5. Haitao Liu
6. Felipe Antonio Chegury Viana
7. Saman Razavi
8. Donald H. Burn

Citations in the corpus

• A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, methodology): “Probability based Mode-pursuing sampling (Wang et al. 2004) Model-independent; global model; use a probability function to take into account local exploitation and global exploration; trade-off between global & local is randomly determined”
• Metamodeling in Multidisciplinary Design Optimization: How Far Have We Really Come? (2014, methodology): “This is the case of algorithms such as the efficient global optimization [64] and enhanced sequential optimization algorithms [92] andmode pursuing sampling [111–113].”
• Review of surrogate modeling in water resources (2012, result): “However, in some studies [e.g., Regis and Shoemaker, 2004; Wang et al., 2004], the approximation model is fitted locally only over a specified (limited) number of design sites (a subset of all available design sites), which are in close vicinity of the point of interest in the explanatory variable s”
• Review of Metamodeling Techniques in Support of Engineering Design Optimization (2007, methodology): “A new discriminative sampling method has been developed when the sampling goal was for optimization instead of global metamodeling [37-39].”
• Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions (2010, methodology): “Mode-pursuing sampling method (MPS): MPS (Sharif et al. 2008; Wang et al. 2004) is a recently developed method, which uses a variation of the objective function to act as a probability density function (PDF) so that more points are generated in areas leading to lower objective function values and fe”