Engineering design applications of surrogate-assisted optimization techniques

Sóbester, András; Forrester, Alexander I. J.; Toal, David J. J.; Tresidder, Es; Tucker, Simon

doi:10.1007/s11081-012-9199-x

Cited by 47 publications

(24 citation statements)

References 30 publications

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“…, N Q, were randomly generated to be uniformly distributed over the feasible sets. Also the latin hypercube design of experiments (Sóbester et al 2014) was tested to generate the sample points, but since the resulting improvements of the success of approximation of an optimal solution-as compared with randomly generated sample points-was negligible we chose to use sample points with randomly generated coordinates over their respective feasible intervals (using Matlab's pseudo-random number generator). Considering the various measures for comparing algorithms discussed in this section, the following experimental procedure, inspired by Montaz Ali et al (2005), was established.…”

Section: Assessment Methodologymentioning

confidence: 99%

Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

et al. 2017

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This paper concerns the solution of a class of mathematical optimization problems with simulation-based objective functions. The decision variables are partitioned into two groups, referred to as variables and parameters, respectively, such that the objective function value is influenced more by the variables than by the parameters. We aim to solve this optimization problem for a large number of parameter settings in a computationally efficient way. The algorithm developed uses surrogate models of the objective function for a selection of parameter settings, for each of which it computes an approximately optimal solution over the domain of the variables. Then, approximate optimal solutions for other parameter settings are computed through a weighting of the surrogate models without requiring additional expensive function evaluations. We have tested the algorithm's performance on a set of global optimization problems differing with respect to both mathematical properties and numbers of variables and parameters. Our results show that it outperforms a standard and often applied approach based on a surrogate model of the objective function over the complete space of variables and parameters.

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Section: Assessment Methodologymentioning

confidence: 99%

Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

et al. 2017

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“…The combination of surrogate models and optimization is a powerful tool when concepts of complex products are to be evaluated using modelling and simulation [11]. The most widely used method that involves surrogate models in the industry today is probably to create a surrogate model of a computationally demanding model and then perform an optimization on the surrogate model.…”

Section: Surrogate Model Assisted Optimizationmentioning

confidence: 99%

“…Sóbester et al [11] go as far as to claim that surrogate-based optimization is one of the most significant advances in engineering design technology. This emphases the importance of and the possibilities that the combination of surrogate models and optimization opens up for development of complex products.…”

Section: Introductionmentioning

confidence: 99%

Efficient Optimization of Complex Products : A Simulation and Surrogate Model Based Approach

Persson

2015

Linköping Studies in Science and Technology. Dissertations

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A heart is not judged by how much you love; but by how much you are loved by others. The Wizard of Oz, 1939 AbstractThis thesis investigates how to use optimization efficiently when complex products are developed. Modelling and simulation are necessary to enable optimization of products, but here it is assumed that verified and validated models of the products and their subsystems are available for the optimization. The focus is instead on how to use the models properly for optimization.Knowledge about several areas is needed to enable optimization of a wide range of products. A few methods from each area are investigated and compared. Some modifications to existing methods and new methods are also proposed and compared to the previous methods.These areas include• Optimization algorithms to ensure that a suitable algorithm is used to solve the problem• Multi-Objective Optimization for products with conflicting objectives• Multi-Disciplinary Optimization when analyses from several models and/or disciplines are needed• Surrogate Models to enable optimization of computationally expensive modelsModern frameworks for optimization of complex products often include more than one of these areas and this is exemplified with the industrial applications that are presented in this thesis, including the design and optimization of industrial robots and aircraft systems.

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“…In our case, the surrogate model is built from design points (input-output data points) calculated from the VPSA simulator . Kriging based algorithms have been widely used for single-objective optimisation but there are other issues involved in the multi-objective setting (Sóbester et al, 2014;Voutchkov & Keane, 2010). We propose a novel transformation of the purity and recovery outputs in the surrogate model training 45 phase, as well as a procedure for dealing with the possible failure in the simulator runs during the search.…”

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confidence: 99%

“…Kriging regression (Jin et al, 2001;Sóbester et al, 2014;Stein, 1999) is a popular surrogate modelling technique for engineering design and is used in this work. Other popular techniques include polynomial response surfaces, radial basis functions (RBF), artificial neural networks (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) (Forrester & Keane, 2009).…”

mentioning

confidence: 99%

Multi-objective optimisation using surrogate models for the design of VPSA systems

Beck

Friedrich

Brandani

et al. 2015

Computers & Chemical Engineering

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Vacuum/pressure swing adsorption is an attractive and often energy efficient separation process for some applications. However, there is often a trade-off between the different objectives: purity, recovery and power consumption. Identifying those trade-offs is possible through use of multi-objective optimisation methods but this is computationally challenging due to the size of the search space and the need for high fidelity simulations due to the inherently dynamic nature of the process. This paper presents the use of surrogate modelling to address the computational requirements of high fidelity simulations needed to evaluate alternative designs. We present SbNSGA-II ALM, surrogate based NSGA-II, a robust and fast multi-objective optimisation method based on kriging surrogate models and NSGA-II with Active Learning MacKay (ALM) design criteria. The method is evaluated by application to an industrially relevant case study: a two column six step system for CO 2 /N 2 separation. A 5 times reduction in computational effort is observed.

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Engineering design applications of surrogate-assisted optimization techniques

Cited by 47 publications

References 30 publications

Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

Efficient solution of many instances of a simulation-based optimization problem utilizing a partition of the decision space

Efficient Optimization of Complex Products : A Simulation and Surrogate Model Based Approach

Multi-objective optimisation using surrogate models for the design of VPSA systems

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