14 research outputs found
MATSuMoTo: The MATLAB Surrogate Model Toolbox For Computationally Expensive Black-Box Global Optimization Problems
MATSuMoTo is the MATLAB Surrogate Model Toolbox for computationally
expensive, black-box, global optimization problems that may have continuous,
mixed-integer, or pure integer variables. Due to the black-box nature of the
objective function, derivatives are not available. Hence, surrogate models are
used as computationally cheap approximations of the expensive objective
function in order to guide the search for improved solutions. Due to the
computational expense of doing a single function evaluation, the goal is to
find optimal solutions within very few expensive evaluations. The multimodality
of the expensive black-box function requires an algorithm that is able to
search locally as well as globally. MATSuMoTo is able to address these
challenges. MATSuMoTo offers various choices for surrogate models and surrogate
model mixtures, initial experimental design strategies, and sampling
strategies. MATSuMoTo is able to do several function evaluations in parallel by
exploiting MATLAB's Parallel Computing Toolbox.Comment: 13 pages, 7 figure
Metamaterial filter design via surrogate optimization
Recently, an increasing research effort has been dedicated to analyse transmission and dispersion properties of periodic metamaterials containing resonators, and to optimize the amplitude of selected acoustic band gaps between consecutive dispersion curves in the Floquet-Bloch spectrum. Potential novel applications of this research are in the design of passive mechanical filters/diodes. The present work proposes a way to interpolate the objective functions in such band gap optimization problems, using Radial Basis Functions. The study is motivated by the high computational effort often needed for an exact evaluation of the original objective functions, when using iterative optimization algorithms. By replacing such functions with surrogate objective functions, well-performing suboptimal solutions can be obtained with a small computational effort. Numerical results demonstrate the feasibility of the approach
Integration of expert knowledge into radial basis function surrogate models
A current application in a collaboration between Chalmers University of Technology and Volvo Group Trucks Technology concerns the global optimization of a complex simulation-based function describing the rolling resistance coefficient of a truck tyre. This function is crucial for the optimization of truck tyres selection considered. The need to explicitly describe and optimize this function provided the main motivation for the research presented in this article. Many optimization algorithms for simulation-based optimization problems use sample points to create a computationally simple surrogate model of the objective function. Typically, not all important characteristics of the complex function (as, e.g., non-negativity)—here referred to as expert knowledge—are automatically inherited by the surrogate model. We demonstrate the integration of several types of expert knowledge into a radial basis function interpolation. The methodology is first illustrated on a simple example function and then applied to a function describing the rolling resistance coefficient of truck tyres. Our numerical results indicate that expert knowledge can be advantageously incorporated and utilized when creating global approximations of unknown functions from sample points
Surrogate Optimization of Deep Neural Networks for Groundwater Predictions
Sustainable management of groundwater resources under changing climatic
conditions require an application of reliable and accurate predictions of
groundwater levels. Mechanistic multi-scale, multi-physics simulation models
are often too hard to use for this purpose, especially for groundwater managers
who do not have access to the complex compute resources and data. Therefore, we
analyzed the applicability and performance of four modern deep learning
computational models for predictions of groundwater levels. We compare three
methods for optimizing the models' hyperparameters, including two surrogate
model-based algorithms and a random sampling method. The models were tested
using predictions of the groundwater level in Butte County, California, USA,
taking into account the temporal variability of streamflow, precipitation, and
ambient temperature. Our numerical study shows that the optimization of the
hyperparameters can lead to reasonably accurate performance of all models (root
mean squared errors of groundwater predictions of 2 meters or less), but the
''simplest'' network, namely a multilayer perceptron (MLP) performs overall
better for learning and predicting groundwater data than the more advanced long
short-term memory or convolutional neural networks in terms of prediction
accuracy and time-to-solution, making the MLP a suitable candidate for
groundwater prediction.Comment: submitted to Journal of Global Optimization; main paper: 25 pages, 19
figures, 1 table; online supplement: 11 pages, 18 figures, 3 table