Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints

Quan, Ning; Yin, Jun; Ng, Szu Hui; Lee, Loo Hay

doi:10.1080/0740817x.2012.706377

Cited by 77 publications

(83 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 1 provides an overview of the most important notations in this paper. We refer to Quan et al (2013) , Ankenman et al (2010) , Yin et al (2011) , andCressie (1993) for further details on stochastic Kriging.…”

Section: Kriging-based Simulation Optimizationmentioning

confidence: 99%

“…Analogous to EQI, TSSO has a search and a replication step (called allocation stage in Quan et al, 2013 ). In the search step of iteration k + 1 , TSSO looks at all unvisited alternatives ( x ∈ \ k ) and simulates the one with maximum modified expected improvement (MEI):…”

Section: Two-stage Sequential Optimization (Tsso)mentioning

confidence: 99%

“…This number is shared between the search and replication steps according to a heuristic ( Quan et al, 2013 ). In the replication step, the algorithm uses the optimal computing budget allocation (OCBA) which is a Bayesian ranking and selection procedure ( Chen,Lin,Yücesan,& Chick,20 0 0 ).…”

Section: Two-stage Sequential Optimization (Tsso)mentioning

confidence: 99%

“…The traditional Efficient global optimization (EGO) approach of Jones, Schonlau, and Welch (1998) is one of the most popular Kriging-based algorithms for optimizing noiseless simulation; in this case, the fitted metamodel is a deterministic Kriging model (see Kleijnen, 2015 , for references on the use of EGO in constrained and multi-objective optimization problems). In stochastic simulation (e.g., discrete event simulation), however, EGO might not be very appropriate since it ignores the noise in the observations, assuming they were sampled with infinite precision ( Quan, Yin, Ng, & Lee, 2013 ). Being aware of this deficiency, researchers have tried to extend EGO for stochastic simulation.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

2015

View full text Add to dashboard Cite

a b s t r a c tIn this article we investigate the unconstrained optimization (minimization) of the performance of a system that is modeled through a discrete-event simulation. In recent years, several algorithms have been proposed which extend the traditional Kriging-based simulation optimization algorithms (assuming deterministic outputs) to problems with noise. Our objective in this paper is to compare the relative performance of a number of these algorithms on a set of well-known analytical test functions, assuming different patterns of heterogeneous noise. We also apply the algorithms to a popular inventory test problem. The conclusions and insights obtained may serve as a useful guideline for researchers aiming to apply Kriging-based algorithms to solve engineering and/or business problems, and may be useful in the development of future algorithms.

show abstract

Section: Kriging-based Simulation Optimizationmentioning

confidence: 99%

Section: Two-stage Sequential Optimization (Tsso)mentioning

confidence: 99%

Section: Two-stage Sequential Optimization (Tsso)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

2015

View full text Add to dashboard Cite

show abstract

“…The EI criterion based algorithm uses interpolating kriging surrogate models and was developed for box constrained problems. Other global-search surrogate-based methods which employ kriging approximations within box constrained regions have been developed by Forrester and Jones (2008) and Quan et al (2013). Radial-basis functions have also been used in the global-search surrogate-based CDFO methods for box-constrained problems (Björkman and Holmström, 2000;Holmström et al, 2008b;Jakobsson et al, 2010;Shoemaker, 2007a,b,c, 2013b).…”

Section: Surrogate Model Based Methodsmentioning

confidence: 99%

Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO

Boukouvala

Misener

Floudas

2016

European Journal of Operational Research

199

View full text Add to dashboard Cite

This manuscript reviews recent advances in deterministic global optimization for Mixed-Integer Nonlinear Programming (MINLP), as well as Constrained DerivativeFree Optimization (CDFO). 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. Both research areas have experienced rapid growth, with a common aim to solve a wide range of real-world problems. We show their individual prerequisites, formulations and applicability, but also point out possible points of interaction in problems which contain hybrid characteristics. Finally, an inclusive and complete test suite is provided for both MINLP and CDFO algorithms, which is useful for future benchmarking.

show abstract

Kriging meta‐model assisted calibration of computational fluid dynamics models

et al. 2016

View full text Add to dashboard Cite

Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data.Keywords: Calibration, computational fluid dynamics, expected improvement, Gaussian process, parameter estimation Correspondence concerning this article should be addressed to Tao Chen at t.chen@surrey.ac.uk. 2 IntroductionComputational fluid dynamics (CFD) simulations are usually large complex computer programs representing real life fluid systems. The complexity of such simulations can make them quite a herculean task to solve, with the run time associated with the simulation being of great concern. Such simulation run time could be for hours or days.1,2 In addition, large scale or complex systems usually place a great demand on computer memory. 3 Computation is especially a problem when the CFD simulation is required to run for a large number of times, for example when sensitivity analysis is carried out 1,4 , or the models are used within a certain optimisation problem. 4,5 Of particular interest in this research project is calibration of CFD models (also known as model parameter estimation), which can be cast into an optimisation problem.CFD model calibration can be defined as the process of adjusting numerical or physical parameters in the CFD model which, on obtaining the optimal values 6 , helps to improve agreement with experimental data. 7,8,9 The parameters in this context are typically turbulence model coefficients or turbulence model choice. The default values given by CFD software providers for turbulence parameters may apply only in standard circumstances. In this paper, calibration is formulated as an optimisation problem to minimise the mismatch between simulated and experimental outputs (responses). The calibration process may need tens to hundreds of simulation runs to evaluate the objective function, the sum of squared errors (where the errors quantify the differences between the simulated and the experimental outputs) over the region of interest, a requirement that is often infeasible for CFD.These difficulties faced by CFD simulations have led to the application of meta-modelling, whi...

show abstract

Simulation optimization via kriging: a sequential search using expected improvement with computing budget constraints

Cited by 77 publications

References 30 publications

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

Comparison of Kriging-Based Methods for Simulation Optimization with Heterogeneous Noise

Global optimization advances in Mixed-Integer Nonlinear Programming, MINLP, and Constrained Derivative-Free Optimization, CDFO

Kriging meta‐model assisted calibration of computational fluid dynamics models

Contact Info

Product

Resources

About