The publication “Tuning algorithms for black-box optimization: State of the art and future perspectives” by Thomas Bartz-Beielstein, Frederik Rehbach, and Margarita Rebolledo will be published as a contribution to the book Black Box Optimization, Machine Learning and No- Free Lunch Theorems. The book is edited by Panos Pardalos, Varvara Rasskazova, and Michael Vrahatis and will be number 170 in Springer Optimization and Its Applications series, see https://www.springer.com/de/book/9783030665142Continue reading
The book Optimization Under Uncertainty with Applications to Aerospace Engineering has recently been published in electronic and print format. It includes our contribution Sequential Parameter Optimization for Mixed-Discrete Problems (Lorenzo Gentile, Thomas Bartz-Beielstein, and Martin Zaefferer).
The publication “Expected Improvement versus Predicted Value in Surrogate-Based Optimization”, written by Frederik Rehbach, Martin Zaefferer, Boris Naujoks, and Thomas Bartz-Beielstein, deals with the correct parameterization for model-based optimization algorithms. The publication is available on Cologne Open Science.Continue reading
Unter normalen Umständen hätte die useR! Conference, die weltgrößte Konferenz für die Anwender der Statistiksprache R, dieses Jahr zum ersten mal parallel an zwei Standorten (St. Louis, USA und München) stattfinden sollen.
Stattdessen gab es aufgrund der von COVID-19 bedingten Umstände eine andere Art von Premiere – ein rein virtuelles Format. Das von Prof. Dr. Thomas Bartz-Beielstein geleitete Institut IDE+A hat einen interessanten Vortrag zu “Visualization of missing data and imputations in time series” beigesteuert. Steffen Moritz ging in dem Vortrag auf neueste Entwicklungen im Bereich Visualisierung von fehlenden Messwerten in Zeitserien ein. Insbesondere auch auf die Möglichkeiten, die das imputeTS R Paket in diesem Bereich bietet.
Alle Vorträge der diesjährigen useR! sind auf dem Youtube Channel des R-Consortium online verfügbar.
Information zum OPUS4-Dokumentenserver: Folgendes Dokument wurde auf dem OPUS4-Dokumentenserver freigegeben: Benchmarking in Optimization: Best Practice and Open Issues.
This survey compiles ideas and recommendations from more than a dozen
researchers with different backgrounds and from different institutes around the
world. Promoting best practice in benchmarking is its main goal. The article
discusses eight essential topics in benchmarking: clearly stated goals, well-
specified problems, suitable algorithms, adequate performance measures,
thoughtful analysis, effective and efficient designs, comprehensible
presentations, and guaranteed reproducibility. The final goal is to provide
well-accepted guidelines (rules) that might be useful for authors and
reviewers. As benchmarking in optimization is an active and evolving field of
research this manuscript is meant to co-evolve over time by means of periodic
The PDF version of this survey is available here and will be published on arXiv soon.
“Surrogate assisted optimization of particle reinforced metal matrix composites” (L. Gentile, M. Zaefferer, D. Giugliano, H. Chen, T. Bartz-Beielstein)
“Comparison of Parallel Surrogate-Assisted Optimization Approaches” (F. Rehbach, M. Zaefferer, J. Stork, T. Bartz-Beielstein)
have been accepted as full papers. 100% of our papers, i.e., two out of two, were successful. We are looking forward to giving the presentations in Kyoto!
An online version of the paper “Optimization via multimodel simulation” (https://doi.org/10.1007/s00158-018-1934-2) written by Thomas Bartz-Beielstein, Martin Zaefferer, and Quoc Cuong Pham was published today. This research paper was published in the journal “Structural and Multidisciplinary Optimization“.
“Increasing computational power and the availability of 3D printers provide new tools for the combination of modeling and experimentation. Several simulation tools can be run independently and in parallel, e.g., long running computational fluid dynamics simulations can be accompanied by experiments with 3D printers. Furthermore, results from analytical and data-driven models can be incorporated. However, there are fundamental differences between these modeling approaches: some models, e.g., analytical models, use domain knowledge, whereas data-driven models do not require any information about the underlying processes. At the same time, data-driven models require input and output data, but analytical models do not. The optimization via multimodel simulation (OMMS) approach, which is able to combine results from these different models, is introduced in this paper. We believe that OMMS improves the robustness of the optimization, accelerates the optimization-via-simulation process, and provides a unified approach. Using cyclonic dust separators as a real-world simulation problem, the feasibility of this approach is demonstrated and a proof-of-concept is presented. Cyclones are popular devices used to filter dust from the emitted flue gasses. They are applied as pre-filters in many industrial processes including energy production and grain processing facilities. Pros and cons of this multimodel optimization approach are discussed and experiences from experiments are presented.”
Combined simulation Multimodeling Simulation-based optimization Metamodel Multi-fidelity optimization Stacking Response surface methodology 3D printing Computational fluid dynamics
Cite this article as
Bartz-Beielstein, T., Zaefferer, M. & Pham, Q.C. Struct Multidisc Optim (2018). https://doi.org/10.1007/s00158-018-1934-2
Springer Berlin Heidelberg
The paper “In a Nutshell: Sequential Parameter Optimization” has been assigned the permanent arXiv identifier 1712.04076 and is available at:
Date: Tue, 12 Dec 2017 00:03:45 GMT (2255kb,D)
Title: In a Nutshell: Sequential Parameter Optimization
Authors: Thomas Bartz-Beielstein, Lorenzo Gentile, Martin Zaefferer
Categories: cs.MS cs.AI math.OC
Comments: Version 12/2017
The performance of optimization algorithms relies crucially on their
parameterizations. Finding good parameter settings is called algorithm tuning.
Using a simple simulated annealing algorithm, we will demonstrate how
optimization algorithms can be tuned using the sequential parameter
optimization toolbox (SPOT). SPOT provides several tools for automated and
interactive tuning. The underling concepts of the SPOT approach are explained.
This includes key techniques such as exploratory fitness landscape analysis and
response surface methodology. Many examples illustrate how SPOT can be used for
understanding the performance of algorithms and gaining insight into
algorithm’s behavior. Furthermore, we demonstrate how SPOT can be used as an
optimizer and how a sophisticated ensemble approach is able to combine several
meta models via stacking.
The article “Metamodel-based optimization of hot rolling processes in the metal industry” was published in The International Journal of Advanced Manufacturing. A full-text view-only version of this paper is available. All readers of this article via the shared link will also be able to use Enhanced PDF features such as annotation tools, one-click supplements, citation file exports and article metrics. Here is the link: