Category Archives: Surrogate Models

@TH_Koeln offers 2 #PhD Positions in #optimization @UTOPIAE_network hosted at SPOTSeven lab

Further information:

Click and Read! Free Access: Continuous and Discrete Global #Surrogate #Optimization

http://dx.doi.org/10.1016/j.asoc.2017.01.039Just click and read! Everybody can use the following personal article link, which will provide free access to the article “Model-based methods for continuous and discrete global optimization” (Thomas Bartz-Beielstein, Martin Zaefferer), and is valid for 50 days, until April 11, 2017:
https://authors.elsevier.com/a/1Ub295aecSVmv2

Here are some highlights:

  • Up-to-date survey and comprehensive taxonomy of surrogate model based optimization algorithms.
  • Covers continuous and discrete/combinatorial search spaces.
  • Presents six strategies for dealing with discrete data structures.
  • New strategy for model selection and combination in surrogate model-based optimization.
  • Outlook on important challenges (model selection, dimensionality, benchmarks, definiteness) and research directions.

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@UTOPIAE_network @TH_Koeln: New Link

Local information from TH Köln about the “Uncertainty Treatment and OPtimisation In Aerospace Engineering” (UTOPIAE) project can be accessed online via http://www.gm.fh-koeln.de/utopiae

mariecurielogoeu

This project has received funding from the biggest European Union Research and Innovation Programme Horizon 2020  HORIZON 2020 Website

#Surrogate #Models for Global and #Combinatorial #Optimization (#Kriging, #CFD)

The accepted manuscript (unformatted and unedited PDF) of the article “Model-based Methods for Continuous and Discrete Global Optimization” (T. Bartz-Beielstein, M. Zaefferer) is now available online at:
http://dx.doi.org/10.1016/j.asoc.2017.01.039
Article reference: ASOC4033
Journal title: Applied Soft Computing Journal
Corresponding author: Prof. Thomas Bartz-Beielstein
First author: Prof. Thomas Bartz-Beielstein
Accepted manuscript available online: 8-FEB-2017
DOI information: 10.1016/j.asoc.2017.01.039
A preprint can be downloaded from “Cologne Open Science”: urn:nbn:de:hbz:832-cos4-4356

#Surrogate #Metamodels, and #MachineLearning article online: Model-based Methods for Continuous and Discrete Global #Optimization

http://www.sciencedirect.com/science/article/pii/S1568494617300546

http://www.sciencedirect.com/science/article/pii/S1568494617300546

The article “Model-based Methods for Continuous and Discrete Global Optimization” by T. Bartz-Beielstein and M. Zaefferer is available online: http://www.sciencedirect.com/science/article/pii/S1568494617300546
A preprint can be downloaded from “Cologne Open Science”: urn:nbn:de:hbz:832-cos4-4356
Abstract:
The use of surrogate models is a standard method for dealing with complex real-world optimization problems. The first surrogate models were applied to continuous optimization problems. In recent years, surrogate models gained importance for discrete optimization problems. This article takes this development into consideration. The first part presents a survey of model-based methods, focusing on continuous optimization. It introduces a taxonomy, which is useful as a guideline for selecting adequate model-based optimization tools. The second part examines discrete optimization problems. Here, six strategies for dealing with discrete data structures are introduced. A new approach for combining surrogate information via stacking is proposed in the third part. The implementation of this approach will be available in the open source R package SPOT2. The article concludes with a discussion of recent developments and challenges in continuous and discrete application domains.

SPOTSeven Lab: #Optimization of a gas distribution system design for electrostatic precipitators

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Here are some photos from today’s meeting with engineers from Steinmüller Babcock Environment. Frederik Rehbach is presenting results from the case study “Optimization of a gas distribution system design for electrostatic precipitators”.