Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

Müller, Juliane; Piché, Robert

doi:10.1007/s10898-010-9620-y

Cited by 93 publications

(39 citation statements)

References 25 publications

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“…MATSuMoTo uses the Dempster-Shafer theory to combine the models and takes advantage of correlation coefficients, maximum absolute error, median absolute deviation and root mean square error to calculate the weight w r for each model, based on the performed objective function evaluations in the procedure [52]. Naturally, the number of surrogate models in the mix results in a larger number of models that need to be updated in each loop in the procedure and puts higher demands to the computational effort.…”

Section: Surrogate Modelmentioning

confidence: 99%

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

et al. 2018

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Abstract:The increasing desire for using renewable energy sources throughout the world has resulted in a considerable amount of research into and development of concepts for wave energy converters. By now, many different concepts exist, but still, the wave energy sector is not at a stage that is considered commercial yet, primarily due to the relatively high cost of energy. A considerable amount of the wave energy converters are floating structures, which consequently need mooring systems in order to ensure station keeping. Despite being a well-known concept, mooring in wave energy application has proven to be expensive and has a high rate of failure. Therefore, there is a need for further improvement, investigation into new concepts and sophistication of design procedures. This study uses four Danish wave energy converters, all considered as large floating structures, to investigate a methodology in order to find an inexpensive and reliable mooring solution for each device. The study uses a surrogate-based optimization routine in order to find a feasible solution in only a limited number of evaluations and a constructed cost database for determination of the mooring cost. Based on the outcome, the mooring parameters influencing the cost are identified and the optimum solution determined.

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Section: Surrogate Modelmentioning

confidence: 99%

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

et al. 2018

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“…Müller and Piché (2011) introduced one such combination based on Dempster-Shafer theory, and Müller and Shoemaker (2014) parallelized it by adding random sampling similar to that of Regis and Shoemaker (2007b). RosalesPerez et al (2013) have experimented with an ensemble of SVM surrogates each with a different hyperparameters, and found good performance for the NSGA-II evolutionary multiobjective algorithm.…”

Section: Multiple Surrogatesmentioning

confidence: 99%

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

204

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Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

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“…There are also mixture models (also known as ensemble models) that exploit information from several different surrogate model types (Goel et al, 2007;Müller and Piché, 2011;Müller and Shoemaker, 2014;Viana et al, 2009). In general any type of surrogate model may be used in a surrogate model optimization algorithm.…”

Section: Surrogate Modelsmentioning

confidence: 99%

“…Regis andShoemaker (2007, 2013) also use RBF models and new function evaluation points are selected by a stochastic method. Müller and Piché (2011) developed a framework for automatically computing ensembles of various surrogate model types and Müller and Shoemaker (2014) extended the study to investigate the influence of different sampling strategies on the solution quality. Here for the first time we apply a state-of-the-art RBF surrogate optimization algorithm to the problem of land surface emissions of methane and describe the results.…”

Section: Introductionmentioning

confidence: 99%

CH<sub>4</sub> parameter estimation in CLM4.5bgc using surrogate global optimization

et al. 2015

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Abstract. Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH 4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near-optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH 4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50 %. The methane emission predictions of the CLM using the optimized parameters agree better with the observed methane emission data in northern and tropical latitudes. With the optimized parameters, the methane emission predictions are higher in northern latitudes than when the default parameters are used. For the tropics, the optimized parameters lead to lower emission predictions than the default parameters.

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Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

Abstract: Global optimization, Mixture surrogate models, Dempster-Shafer theory, Response surface,

Cited by 93 publications

References 25 publications

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

Parallel surrogate-assisted global optimization with expensive functions – a survey

CH<sub>4</sub> parameter estimation in CLM4.5bgc using surrogate global optimization

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Mixture surrogate models based on Dempster-Shafer theory for global optimization problems

Abstract: Global optimization, Mixture surrogate models, Dempster-Shafer theory, Response surface,

Cited by 93 publications

References 25 publications

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

Cost Optimization of Mooring Solutions for Large Floating Wave Energy Converters

Parallel surrogate-assisted global optimization with expensive functions – a survey

CH&lt;sub&gt;4&lt;/sub&gt; parameter estimation in CLM4.5bgc using surrogate global optimization

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Product

Resources

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CH<sub>4</sub> parameter estimation in CLM4.5bgc using surrogate global optimization