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Combining metamodel techniques and Bayesian selection procedures to derive computationally efficient simulation-based optimization algorithms

Carolina Osorio, Hoda Bidkhori
2012 Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)  
In order to derive an SO algorithm that achieves a good trade-off between detail, realism and computational efficiency, the metamodel combines information from a high-resolution simulator with information  ...  This paper presents a simulation-based optimization (SO) algorithm for nonlinear problems with general constraints and computationally expensive evaluation of objective functions.  ...  The metamodel combines information from a low-resolution but computationally efficient analytical queueing model with high-resolution simulated data.  ... 
doi:10.1109/wsc.2012.6465111 dblp:conf/wsc/OsorioB12 fatcat:psdgxi6xbzd3fjgzf5jpei4qza

Numerical assessment of metamodelling strategies in computationally intensive optimization

Saman Razavi, Bryan A. Tolson, Donald H. Burn
2012 Environmental Modelling & Software  
Numerical results show that metamodelling is not always an efficient and reliable approach to optimizing computationally intensive problems.  ...  A metamodel-enabled optimizer approximates the objective (or constraint) function in a way that eliminates the need to always evaluate this function via a computationally expensive simulation model.  ...  We would like to thank the guest editors and the four anonymous reviewers for their insightful comments which significantly improved our manuscript.  ... 
doi:10.1016/j.envsoft.2011.09.010 fatcat:owdl5mvfojbl5aa4kto7zedxym

Review of surrogate modeling in water resources

Saman Razavi, Bryan A. Tolson, Donald H. Burn
2012 Water Resources Research  
A wide variety of methods and tools have been introduced for surrogate modeling aiming to develop and utilize computationally more efficient surrogates of high-fidelity models mostly in optimization frameworks  ...  1] Surrogate modeling, also called metamodeling, has evolved and been extensively used over the past decades.  ...  The authors would like to thank the reviewers, Holger Maier, Uwe Ehret, and the two anonymous reviewers, for their extensive and very helpful comments and suggestions, which significantly improved this  ... 
doi:10.1029/2011wr011527 fatcat:rvib7d3zhjei3olhldarvpyhpy

Efficient Transport Simulation With Restricted Batch-Mode Active Learning

Francisco Antunes, Bernardete Ribeiro, Francisco C. Pereira, Rui Gomes
2018 IEEE transactions on intelligent transportation systems (Print)  
This allows us to explore the simulators' input space with fewer data points and in parallel, and thus in a more efficient way, while avoiding computationally expensive simulation runs in the process.  ...  To tackle this problem, simulation metamodels are often used to approximate the simulators' results.  ...  to address in a near future, as well as the combination of both simulation and optimization metamodels using active learning strategies.  ... 
doi:10.1109/tits.2018.2842695 fatcat:wj5nh5bppnbhxgxy6ptuo2zz7i

Deep Gaussian Process metamodeling of sequentially sampled non-stationary response surfaces

Vincent Dutordoir, Nicolas Knudde, Joachim van der Herten, Ivo Couckuyt, Tom Dhaene
2017 2017 Winter Simulation Conference (WSC)  
Therefore, metamodeling aims to approximate the simulation response with a cheap-to-evaluate mathematical approximation, learned from a limited set of simulator evaluations.  ...  We present the application of a novel kernel-based technique, known as Deep Gaussian Processes, which is better able to cope with these difficulties.  ...  The metamodel may then be used instead of the computationally expensive simulator to derive characteristics of the problem at hand or to search for optima.  ... 
doi:10.1109/wsc.2017.8247911 dblp:conf/wsc/DutordoirKHCD17 fatcat:s2v4aezj25bz7ivg2qna3mxn4m

Sequential sensitivity analysis of expensive black-box simulators with metamodelling

Tom Van Steenkiste, Joachim van der Herten, Ivo Couckuyt, Tom Dhaene
2018 Applied Mathematical Modelling  
In this paper we present, discuss and evaluate a novel algorithm for sequential variance-based and derivative-based sensitivity analysis of expensive blackbox simulators using metamodelling.  ...  Hence, metamodelling techniques are used to reduce the computational burden.  ...  The metamodelling accuracy and efficiency can be significantly improved using sequential sampling algorithms.  ... 
doi:10.1016/j.apm.2018.05.023 fatcat:v3hfzcm6erbu5dl3drzw343zke

Metamodel based high-fidelity stochastic analysis of composite laminates: A concise review with critical comparative assessment

S. Dey, T. Mukhopadhyay, S. Adhikari
2017 Composite structures  
Both individual and combined variations of input parameters have been considered to account for the effect of low and high dimensional input parameter spaces in the surrogate based uncertainty quantification  ...  The crucial issue regarding influence of sampling techniques on the performance of metamodel based uncertainty quantification has been addressed as an integral part of this article. natural frequency ∂  ...  D-optimal design method is observed to be the most computationally efficient and accurate compared to other design of experiment algorithms.  ... 
doi:10.1016/j.compstruct.2017.01.061 fatcat:s4jsrr33xffkfnay7gcjbtmfse

Continuous optimization via simulation using Golden Region search

Alireza Kabirian, Sigurdur Ólafsson
2011 European Journal of Operational Research  
For a comparison of Subset Selection methods and Bayesian Selection methods, see Gupta and Miescke (2002) .  ...  Commercial Softwares of Simulation Optimization Chapter 3: Hybrid Probabilistic Search In this chapter, we propose an algorithm that merges Ranking and Selection procedures with a large class of Random  ... 
doi:10.1016/j.ejor.2010.09.002 fatcat:rwcven6pwjbnbiki3cjsynmaqu

Simulation optimization: A tutorial overview and recent developments in gradient-based methods

Marie Chau, Michael C. Fu, Huashuai Qu, Ilya O. Ryzhov
2014 Proceedings of the Winter Simulation Conference 2014  
Statistical ranking & selection (R&S) procedures, where here is meant to include efficient simulation budget allocation methods such as optimal computing budget allocation (OCBA), address discrete problems  ...  For a far more detailed description of many of the techniques described here, the reader is encouraged to consult the two books, Handbook of Simulation Optimization (Fu 2014a) and Optimal Learning (Powell  ...  In the latter 2 SIMULATION OPTIMIZATION METHODS Ranking and Selection Ranking and selection (R&S) can be viewed as the most fundamental class of simulation optimization problems.  ... 
doi:10.1109/wsc.2014.7019875 dblp:conf/wsc/ChauFQR14 fatcat:jzkyzx6kbnfephf6xtmea7dxoq

Gaussian Process Regression Tuned by Bayesian Optimization for Seawater Intrusion Prediction

George Kopsiaftis, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, Aristotelis Mantoglou
2019 Computational Intelligence and Neuroscience  
A Bayesian optimization method is applied to all the regressors, to maximize their efficiency in the prediction of seawater intrusion.  ...  Gaussian process regression is a nonparametric kernel-based probabilistic model able to handle complex relations between input and output.  ...  [23] combined a simulation-optimization procedure with ANNs to develop an efficient model for the multiobjective management of groundwater lenses in small islands.  ... 
doi:10.1155/2019/2859429 fatcat:a75p7ipuvnahjg6pay7jturbv4

Advanced RF and Microwave Design Optimization: A Journey and a Vision of Future Trends

Jose E. Rayas-Sanchez, Slawomir Koziel, John W. Bandler
2021 IEEE Journal of Microwaves  
optimization, and formal cognition-driven space mapping approaches, assisted by Bayesian and machine learning techniques.  ...  subject to statistical uncertainty and varying operating or environmental conditions; 2) the computationally-efficient EM-driven multi-objective design optimization in high-dimensional design spaces including  ...  Flowchart of the surrogate-assisted procedure for computationally efficient MO design [148] .  ... 
doi:10.1109/jmw.2020.3034263 fatcat:a64hobxhfzhe3f2stmhkewvgca

Surrogate-assisted multicriteria optimization: Complexities, prospective solutions, and business case

Richard Allmendinger, Michael T. M. Emmerich, Jussi Hakanen, Yaochu Jin, Enrico Rigoni
2017 Journal of Multi-Criteria Decision Analysis  
For example, complexities may relate to the appropriate selection of metamodels for the individual objective functions, extensive training time of surrogate models, or the optimal use of many-core computers  ...  This leads to further complexities, namely how to validate statistically and apply the techniques developed to real-world problems.  ...  Coello Coello for actively taking part in those discussions and Dr. Karthik Sindhya for his help related to creation of a virtual library for benchmarking.  ... 
doi:10.1002/mcda.1605 fatcat:5ew65lket5azppoo4uw546wdna

Bayesian Simulation and Decision Analysis: An Expository Survey

Jason R. W. Merrick
2009 Decision Analysis  
simulated system, and applications of Bayesian simulation methods.  ...  T he aim of this expository survey on Bayesian simulation is to stimulate more work in the area by decision analysts.  ...  Acknowledgments This material is partially based on work supported by the National Science Foundation under Grants SES 0213627 and SES 0213700.  ... 
doi:10.1287/deca.1090.0151 fatcat:t2wkwepqy5d3fku6hp7snerdga

Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions

Songqing Shan, G. Gary Wang
2009 Structural And Multidisciplinary Optimization  
functions and promising ideas behind non-gradient optimization algorithms.  ...  This paper provides a survey on related modeling and optimization strategies that may help to solve High-dimensional, Expensive (computationally), Blackbox (HEB) problems.  ...  Acknowledgments Funding supports from Canada Graduate Scholarships (CGS) and Natural Science and Engineering Research Council (NSERC) of Canada are gratefully acknowledged.  ... 
doi:10.1007/s00158-009-0420-2 fatcat:xyxrxedzvzdmfpwpbdyovabdze

Kriging-sparse Polynomial Dimensional Decomposition surrogate model with adaptive refinement

Andrea F. Cortesi, Ghina Jannoun, Pietro M. Congedo
2019 Journal of Computational Physics  
This because a limited amount of simulations is available to build a sufficiently accurate metamodel of the quantities of interest.  ...  Firstly, a technique which couples Universal Kriging with sparse Polynomial Dimensional Decomposition (PDD) to build a metamodel with improved accuracy.  ...  The second algorithm is based on the metamodeling error (see algorithm 2) and instead selects the most relevant terms according to their contribution to the global metamodeling error computed with a leave-one-out  ... 
doi:10.1016/ fatcat:xbocgdlaxbhlphoydiqqy3upuy
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