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A Kriging-Assisted Multi-Objective Constrained Global Optimization Method for Expensive Black-Box Functions

Yaohui Li, Jingfang Shen, Ziliang Cai, Yizhong Wu, Shuting Wang
2021 Mathematics  
Then, kriging-based estimated target, RMSE (root mean square error), and feasibility probability are used to form three objectives, which are optimized to generate the Pareto frontier set through multi-objective  ...  The sample data obtained from the expensive function evaluation is first used to construct or update the kriging model in each cycle.  ...  Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/math9020149 fatcat:2budp5wxw5anfko2yzd3olefci

Recent developments in metamodel based robust black-box simulation optimization: An overview

Nader Ale Ebrahim, Amir Parnianifard, A Azfanizam, M Ariffin, M Ismail
2018 Figshare  
In this state-of the art review paper, a systematic qualitative and quantitative review is implemented among Metamodel Based Robust Simulation Optimization (MBRSO) for black-box and expensive simulation  ...  This context is focused on the management of uncertainty, particularly based on the Taguchi worldview on robust design and robust optimization methods in the class of dual response methodology when simulation  ...  Ale Ebrahim, "Recent developments in metamodel based robust black-box simulation optimization: An overview," Decision Science Letters, vol. 8, no. 1, pp. 17-44, 2019.  ... 
doi:10.6084/m9.figshare.6466625 fatcat:va7fpxohdvgn7o4boceztyqupe

High-Fidelity Surrogate Based Multi-Objective Optimization Algorithm

Adel Younis, Zuomin Dong
2022 Algorithms  
dynamics (CFD), surrogate models are found to be a promising endeavor, particularly for the optimization of complex engineering design problems involving black box functions.  ...  Utilizing data samples taken from the feasible design region, the algorithm creates three surrogate models.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a15080279 fatcat:ctpnuazi3vhiribnpc2vfqupwm

Recent developments in metamodel based robust black-box simulation optimization: An overview

Amir Parnianifard, A.S. Azfanizam, M.K.A. Ariffin, M.I.S. Ismail, Nader Ale Ebrahim
2019 Decision Science Letters  
Expensive simulation running and expensive analysis of processes are often considered black-box function.  ...  Kriging is an interpolation method which could cover deterministic data in a black-box presentation, and it is highly flexible due to ability in employing a various range of correlation functions.  ... 
doi:10.5267/j.dsl.2018.5.004 fatcat:j5ihfxwnlfesbcsuyyli622b44

A New Approach for Low-Dimensional Constrained Engineering Design Optimization Using Design and Analysis of Simulation Experiments

Amir Parnianifard, Ratchatin Chancharoen, Gridsada Phanomchoeng, Lunchakorn Wuttisittikulkij
2020 International Journal of Computational Intelligence Systems  
This paper presents an adaptive algorithm called the Surrogate-Based Constrained Global-Optimization (SCGO) method to solve black-box constrained simulation-based optimization problems involving computationally  ...  Then, an adaptive approach is provided to improve the optimal results sequentially while enforcing a feasible solution.  ...  CONFLICTS OF INTEREST The authors declare that they have no conflicts of interests. AUTHORS' CONTRIBUTIONS All authors have contributed equally to this study.  ... 
doi:10.2991/ijcis.d.201014.001 fatcat:pi6xj6jcmnhnpdpnnx5r7ryo2y

An Improved Blind Kriging Surrogate Model for Design Optimization Problems

Hau T. Mai, Jaewook Lee, Joowon Kang, H. Nguyen-Xuan, Jaehong Lee
2022 Mathematics  
In addition, an infill strategy is developed based on the probability of feasibility, penalization, and constrained expected improvement for updating blind Kriging metamodels of the objective and constraints  ...  Surrogate modeling techniques are widely employed in solving constrained expensive black-box optimization problems.  ...  The universal Kriging (UK) model postulates a combination of the regression function and a stochastic process, as demonstrated in Equation ( 1 ) Y(x) = f (x) + Z(x), (1) where Y(x) represents a black-box  ... 
doi:10.3390/math10162906 fatcat:zufu7kyt4zecrddnglpl3gbux4

An algorithm for the use of surrogate models in modular flowsheet optimization

José A. Caballero, Ignacio E. Grossmann
2008 AIChE Journal  
The black box modules are substituted by metamodels based on a kriging interpolation that assumes that the errors are not independent but a function of the independent variables.  ...  A Kriging metamodel uses a non Euclidean measure of distance that avoid sensitivity to the units of measure.  ...  In this work, we develop an algorithm based on fitting response surfaces -using a kriging metamodel-for the optimization of constrained-noise black box models.  ... 
doi:10.1002/aic.11579 fatcat:2gsz6mz7yjcxfk5grghuccushu


G. Gary Wang, S. Shan
2011 Proceedings of the Canadian Engineering Education Association (CEEA)  
The computation burden is often caused by expensive analysis and simulation processes.  ...  This work will review the current state-of-the-art on metamodeling-based techniques in support of product design.  ...  Metamodeling for Function Properties Currently metamodeling is only used for approximating the design variables and their performances, which are often used as an output of the "black-box" functions.  ... 
doi:10.24908/pceea.v0i0.3940 fatcat:uteiaoju2bckxntuscqsv3gjra

Comprehensive Analysis of Structural Safety and Durability for Aero-Engine

Yan Li, Qi Gong, Duo Su
2014 Procedia Engineering  
probabilities of key aero-engine structures, probabilistic risk assessment based on the Monte Carlo Radius-Outside Sampling (MCROS) in combination with the Kriging is used, then random factors such as  ...  Example of probabilistic risk assessment based on fatigue for lower pressure compressor disk of aero-engine demonstrates the applicability, versatility and accuracy of the approach.  ...  Acknowledgements We would like to thank China Aero-Polythchnology Establishment for funding this work, in particular Xudong Li for his useful comment and for supporting development of ideas in this paper  ... 
doi:10.1016/j.proeng.2014.09.062 fatcat:cuygl6esdnfqhjfvou7anecxem


Yaohui Li
2016 International Journal on Smart Sensing and Intelligent Systems  
Efficient Global Optimization (EGO) algorithm with Kriging model is stable and effective for an expensive black-box function.  ...  In order to better solve a black-box unconstrained optimization problem, this paper introduces a new EGO method named improved generalized EGO (IGEGO), in which two targets will be achieved: using Kriging  ...  In addition, the IGEGO and GEGO methods are adopted to deal with the black-box optimization problem.  ... 
doi:10.21307/ijssis-2017-902 fatcat:k3nqawbsfndvbckfhjrkiopppq

State-of-the-Art and Comparative Review of Adaptive Sampling Methods for Kriging

Jan N. Fuhg, Amélie Fau, Udo Nackenhorst
2020 Archives of Computational Methods in Engineering  
Hence, in order to build proficient kriging models with as few samples as possible adaptive sampling strategies have gained considerable attention.  ...  A review of adaptive schemes for kriging proposed in the literature is presented in this article.  ...  Box 10 Adaptive MASA algorithm Adaptive Methods using Query-by-Committee-Based Exploitation Only one method based on query-by-committee is studied here because the essential process is similar in many  ... 
doi:10.1007/s11831-020-09474-6 fatcat:o6oy54zh2ng3toacikuv54m4va

A survey on handling computationally expensive multiobjective optimization problems using surrogates: non-nature inspired methods

Mohammad Tabatabaei, Jussi Hakanen, Markus Hartikainen, Kaisa Miettinen, Karthik Sindhya
2015 Structural And Multidisciplinary Optimization  
In order to deal with the high computational cost, surrogate-based methods are commonly used in the literature.  ...  Based on the comparison, we recommend the adaptive framework to tackle the aforementioned challenges.  ...  In [70] , an optimization-based method is developed which is applied to solve an MOP of injection-molding process with five black-box objective functions and three decision variables.  ... 
doi:10.1007/s00158-015-1226-z fatcat:2smcqe66xraunb2vq56gs72hpu

Reliability optimization design method based on multi-level surrogate model

Yong-Hua Li, Xiao-Jia Liang, Si-Hu Dong
2020 Eksploatacja i Niezawodnosc  
Meanwhile, a reliability design optimization method based on multi-level surrogate model is studied by dealing with the reliability constraints with an adaptive reliability penalty function.  ...  A multi-point addition criterion of genetic-algo-• rithm-based is introduced to the Kriging model.  ...  Kriging-based multi-point addition criterion Kriging surrogate model is an interpolation approximation method.  ... 
doi:10.17531/ein.2020.4.7 fatcat:n2hedtgmv5bwbp2ktaqknpmewq

Efficient global optimization for high-dimensional constrained problems by using the Kriging models combined with the partial least squares method

Mohamed Amine Bouhlel, Nathalie Bartoli, Rommel G. Regis, Abdelkader Otsmane, Joseph Morlier
2018 Engineering optimization (Print)  
Conclusions This paper introduced the SEGOKPLS and SEGOKPLS+K algorithms adapted to high-dimensional constrained surrogate based optimization methods using expensive black-box functions.  ...  For instance, Li et al (2016) developed a Kriging-based algorithm for constrained black-box optimization that involves two phases.  ... 
doi:10.1080/0305215x.2017.1419344 fatcat:aumlhhm7d5g35jaes7afjgh2li

High-Precision Kriging Modeling Method Based on Hybrid Sampling Criteria

Junjun Shi, Jingfang Shen, Yaohui Li
2021 Mathematics  
To this end, a high-precision Kriging modeling method based on hybrid sampling criteria (HKM-HS) is proposed to solve this problem.  ...  In the HKM-HS method, two infilling sampling strategies based on MSE (Mean Square Error) are optimized to obtain new candidate points.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/math9050536 fatcat:6degzbpxcfhinc62dfr2jrw3gi
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