COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and Black-Box optimization techniques

The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.

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Online parameter estimation for cyber-physical production systems based on mixed integer nonlinear programming, process mining and black-box optimization techniques

at - Automatisierungstechnik ◽

10.1515/auto-2017-0124 ◽

2018 ◽

Vol 66 (4) ◽

pp. 331-343 ◽

Cited By ~ 2

Author(s):

Jens Otto ◽

Birgit Vogel-Heuser ◽

Oliver Niggemann

Keyword(s):

Production Systems ◽

Process Mining ◽

Black Box ◽

Optimization Techniques ◽

Mixed Integer ◽

Software Components ◽

Integer Nonlinear Programming ◽

Engineering Effort ◽

Free Parameters ◽

Cyber Physical Production Systems

AbstractCyber-Physical Production Systems (CPPS) should adapt to new products or product variants efficiently and without extensive manual engineering effort. In comparison to rewriting the automation software for each adaption, manual engineering effort can be reduced by reusable software components with free parameters, which must be adjusted to individual production scenarios. This paper introduces CyberOpt Online, a novel online parameter estimation approach for reusable automation software components. In contrast to classic mathematical modeling approaches, such as Mixed Integer Nonlinear Programming (MINLP), our approach requires no predefined models that represent the system. Models, e. g., of the energy consumption of CPPS, are learned automatically from data observed during the operation of the production system. Therefore, the manual engineering effort is minimized as postulated by the paradigm of CPPS. The presented approach combines MINLP, process mining and black-box optimization techniques for calculating optimal timing parameter configurations for automation software components with free parameters in the domain of discrete manufacturing.

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Implementation of Black Box Models for Internal Ballistics Optimization Using an Artificial Neural Network

Mathematical Problems in Engineering ◽

10.1155/2018/1039163 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Jie Chen ◽

JingYin Li ◽

ShuangXi Li ◽

YunXiang You

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Rotational Speed ◽

Black Box ◽

Optimization Techniques ◽

Low Energy ◽

Internal Ballistics ◽

Box Models ◽

Artificial Neural ◽

Black Box Models

The process of UUV delivery is a typical nonlinear transient dynamic phenomenon, which is generally described by the internal ballistic model. Evaluation of optimal internal ballistics parameters is a key step for promoting ballistic weapon performance under given launch constraints. Hence, accurate and efficient optimization techniques are required in ballistics technology. In this study, an artificial neural network (ANN) is used to simplify the process of regression analysis. To this end, an internal ballistics model is built in this study as a black box for a classic underwater launching system, such as a torpedo launcher, based on ANN parameter identification. The established black box models are mainly employed to calculate the velocity of a ballistic body and the torque of a launching pump. Typical internal ballistics test data are adopted as samples for training the ANN. Comparative results demonstrate that the developed black box models can accurately reflect changes in internal ballistics parameters according to rotational speed variations. Therefore, the proposed approach can be fruitfully applied to the task of internal ballistics optimization. The optimization of internal ballistics precision control, optimal control of the launching pump, and optimal low-energy launch control were, respectively, realized in conjunction with the established model using the SHERPA search algorithm. The results demonstrate that the optimized internal ballistics rotational speed curve can achieve the optimization objectives of low-energy launch and peak power while meeting the requirements of optimization constraints.

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Metamodel-based multidisciplinary design optimization methods for aerospace system

Astrodynamics ◽

10.1007/s42064-021-0109-x ◽

2021 ◽

Vol 5 (3) ◽

pp. 185-215

Author(s):

Renhe Shi ◽

Teng Long ◽

Nianhui Ye ◽

Yufei Wu ◽

Zhao Wei ◽

...

Keyword(s):

Design Optimization ◽

Multidisciplinary Design Optimization ◽

Computational Cost ◽

Optimization Methods ◽

Problem Formulation ◽

Black Box ◽

Optimization Techniques ◽

Multidisciplinary Optimization ◽

Multidisciplinary Design ◽

Aerospace Systems

AbstractThe design of complex aerospace systems is a multidisciplinary design optimization (MDO) problem involving the interaction of multiple disciplines. However, because of the necessity of evaluating expensive black-box simulations, the enormous computational cost of solving MDO problems in aerospace systems has also become a problem in practice. To resolve this, metamodel-based design optimization techniques have been applied to MDO. With these methods, system models can be rapidly predicted using approximate metamodels to improve the optimization efficiency. This paper presents an overall survey of metamodel-based MDO for aerospace systems. From the perspective of aerospace system design, this paper introduces the fundamental methodology and technology of metamodel-based MDO, including aerospace system MDO problem formulation, metamodeling techniques, state-of-the-art metamodel-based multidisciplinary optimization strategies, and expensive black-box constraint-handling mechanisms. Moreover, various aerospace system examples are presented to illustrate the application of metamodel-based MDOs to practical engineering. The conclusions derived from this work are summarized in the final section of the paper. The survey results are expected to serve as guide and reference for designers involved in metamodel-based MDO in the field of aerospace engineering.

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Expensive Black-Box Model Optimization via a Gold Rush Policy

Volume 2B: 44th Design Automation Conference ◽

10.1115/detc2018-85881 ◽

2018 ◽

Author(s):

Benson Isaac ◽

Douglas Allaire

Keyword(s):

Global Optimization ◽

Structural Information ◽

Optimization Methods ◽

Gold Rush ◽

Black Box ◽

Optimization Techniques ◽

Benchmark Problems ◽

Expensive Function ◽

Box Models ◽

Bayesian Global Optimization

The optimization of expensive black-box models is a challenging task owing to the lack of analytic gradient information and structural information about the underlying function, and also due to the sheer computational expense. A common approach to tackling such problems is the implementation of Bayesian global optimization techniques. However, these techniques often rely on surrogate modeling strategies that endow the approximation of the underlying expensive function with nonexistent features. Further, these techniques tend to push new queries away from previously queried design points, making it difficult to locate an optimum point that rests near a previous model evaluation. To overcome these issues, we propose a gold rush policy that relies on purely local information to identify the next best design alternative to query. The method employs a surrogate constructed pointwise, that adds no additional features to the approximation. The result is a policy that performs well in comparison to state of the art Bayesian global optimization methods on several benchmark problems. The policy is also demonstrated on a constrained optimization problem using a penalty method.

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Adaptive Dimensionality Reduction for Fast Sequential Optimization With Gaussian Processes

Journal of Mechanical Design ◽

10.1115/1.4043202 ◽

2019 ◽

Vol 141 (7) ◽

Cited By ~ 7

Author(s):

Seyede Fatemeh Ghoreishi ◽

Samuel Friedman ◽

Douglas L. Allaire

Keyword(s):

Global Optimization ◽

Computational Models ◽

Black Box ◽

Optimization Techniques ◽

Sequential Optimization ◽

Benchmark Problems ◽

Box Models ◽

Gradient Based ◽

Bayesian Global Optimization ◽

Knowledge Gradient

Available computational models for many engineering design applications are both expensive and and of a black-box nature. This renders traditional optimization techniques difficult to apply, including gradient-based optimization and expensive heuristic approaches. For such situations, Bayesian global optimization approaches, that both explore and exploit a true function while building a metamodel of it, are applied. These methods often rely on a set of alternative candidate designs over which a querying policy is designed to search. For even modestly high-dimensional problems, such an alternative set approach can be computationally intractable, due to the reliance on excessive exploration of the design space. To overcome this, we have developed a framework for the optimization of expensive black-box models, which is based on active subspace exploitation and a two-step knowledge gradient policy. We demonstrate our approach on three benchmark problems and a practical aerostructural wing design problem, where our method performs well against traditional direct application of Bayesian global optimization techniques.

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Expensive Black-Box Model Optimization Via a Gold Rush Policy

Journal of Mechanical Design ◽

10.1115/1.4042113 ◽

2019 ◽

Vol 141 (3) ◽

Author(s):

Benson Isaac ◽

Douglas Allaire

Keyword(s):

Global Optimization ◽

Structural Information ◽

Optimization Methods ◽

Gold Rush ◽

Black Box ◽

Optimization Techniques ◽

Benchmark Problems ◽

Expensive Function ◽

Box Models ◽

Bayesian Global Optimization

The optimization of black-box models is a challenging task owing to the lack of analytic gradient information and structural information about the underlying function, and also due often to significant run times. A common approach to tackling such problems is the implementation of Bayesian global optimization techniques. However, these techniques often rely on surrogate modeling strategies that endow the approximation of the underlying expensive function with nonexistent features. Further, these techniques tend to push new queries away from previously queried design points, making it difficult to locate an optimum point that rests near a previous model evaluation. To overcome these issues, we propose a gold rush (GR) policy that relies on purely local information to identify the next best design alternative to query. The method employs a surrogate constructed pointwise, that adds no additional features to the approximation. The result is a policy that performs well in comparison to state of the art Bayesian global optimization methods on several benchmark problems. The policy is also demonstrated on a constrained optimization problem using a penalty method.

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