NDLI: Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption

Content Provider	Springer Nature Link
Author	Boukouvala, Fani Hasan, M. M. Faruque Floudas, Christodoulos A.
Copyright Year	2015
Abstract	This paper introduces a novel methodology for the global optimization of general constrained grey-box problems. A grey-box problem may contain a combination of black-box constraints and constraints with a known functional form. The novel features of this work include (i) the selection of initial samples through a subset selection optimization problem from a large number of faster low-fidelity model samples (when a low-fidelity model is available), (ii) the exploration of a diverse set of interpolating and non-interpolating functional forms for representing the objective function and each of the constraints, (iii) the global optimization of the parameter estimation of surrogate functions and the global optimization of the constrained grey-box formulation, and (iv) the updating of variable bounds based on a clustering technique. The performance of the algorithm is presented for a set of case studies representing an expensive non-linear algebraic partial differential equation simulation of a pressure swing adsorption system for $$\hbox {CO}_{2}$$ . We address three significant sources of variability and their effects on the consistency and reliability of the algorithm: (i) the initial sampling variability, (ii) the type of surrogate function, and (iii) global versus local optimization of the surrogate function parameter estimation and overall surrogate constrained grey-box problem. It is shown that globally optimizing the parameters in the parameter estimation model, and globally optimizing the constrained grey-box formulation has a significant impact on the performance. The effect of sampling variability is mitigated by a two-stage sampling approach which exploits information from reduced-order models. Finally, the proposed global optimization approach is compared to existing constrained derivative-free optimization algorithms.
Starting Page	3
Ending Page	42
Page Count	40
File Format	PDF
ISSN	09255001
Journal	Journal of Global Optimization
Volume Number	67
Issue Number	1-2
e-ISSN	15732916
Language	English
Publisher	Springer US
Publisher Date	2015-11-05
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Derivative-free optimization Kriging Quadratic Constrained optimization Sampling reduction Global optimization Optimization Operation Research/Decision Theory Real Functions Computer Science
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Applied Mathematics Management Science and Operations Research Business, Management and Accounting Computer Science Applications

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