TY - RPRT
A1 - Bartz-Beielstein, Thomas
A1 - Zaefferer, Martin
T1 - A Gentle Introduction to Sequential Parameter Optimization
N2 - There is a strong need for sound statistical analysis of simulation and optimization algorithms. Based on this analysis, improved parameter settings can be determined. This will be referred to as tuning. Model-based investigations are common approaches in simulation and optimization. The sequential parameter optimization toolbox (SPOT), which is implemented as a package for the statistical programming language R, provides sophisticated means for tuning and understanding simulation and optimization algorithms. The toolbox includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as classification and regressions trees (CART) and random forest; Gaussian process models (Kriging), and combinations of different meta-modeling approaches. This article exemplifies how an existing optimization algorithm, namely simulated annealing, can be tuned using the SPOT framework.
T3 - CIplus - 1/2012
KW - Optimierung
KW - Globale Optimierung
KW - Simulation
KW - Simulated annealing
KW - Versuchsplanung
KW - Modellierung
KW - Soft Computing
KW - Sequentielle Parameter Optimierung
KW - Parametertuning
KW - Computational Intelligence
Y1 - 2012
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-191
ER -
TY - RPRT
A1 - Breiderhoff, Beate
A1 - Bartz-Beielstein, Thomas
A1 - Naujoks, Boris
A1 - Zaefferer, Martin
A1 - Fischbach, Andreas
A1 - Flasch, Oliver
A1 - Friese, Martina
A1 - Mersmann, Olaf
A1 - Stork, Jörg
T1 - Simulation and Optimization of Cyclone Dust Separators
N2 - Cyclone Dust Separators are devices often used to filter solid particles from flue gas. Such cyclones are supposed to filter as much solid particles from the carrying gas as possible. At the same time, they should only introduce a minimal pressure loss to the system. Hence, collection efficiency has to be maximized and pressure loss minimized. Both the collection efficiency and pressure loss are heavily influenced by the cyclones geometry. In this paper, we optimize seven geometrical parameters of an analytical cyclone model. Furthermore, noise variables are introduced to the model, representing the non-deterministic structure of the real-world problem. This is used to investigate robustness and sensitivity of solutions. Both the deterministic as well as the stochastic model are optimized with an SMS-EMOA. The SMS-EMOA is compared to a single objective optimization algorithm. For the harder, stochastic optimization problem, a surrogate-model-supported SMS-EMOA is compared against the model-free SMS-EMOA. The model supported approach yields better solutions with the same run-time budget.
T3 - CIplus - 4/2013
KW - Soft Computing
KW - Evolutionärer Algorithmus
KW - Mehrkriterielle Optimierung
KW - Entstauber
KW - Simulation
KW - Mehrkriterielle Optimierung
KW - Surrogat-Modellierung
KW - Sequentielle Parameter Optimierung
KW - Zylon Enstauber
KW - Multiobjective Optimization
KW - Multi-Criteria Optimization
KW - Surrogate Modeling
KW - Sequential Parameter Optimization
KW - Cyclone Dust Separator
Y1 - 2013
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-470
ER -
TY - INPR
A1 - Zaefferer, Martin
A1 - Gaida, Daniel
A1 - Bartz-Beielstein, Thomas
T1 - Multi-fidelity Modeling and Optimization of Biogas Plants
N2 - An essential task for operation and planning of biogas plants is the optimization of substrate feed mixtures. Optimizing the monetary gain requires the determination of the exact amounts of maize, manure, grass silage, and other substrates. Accurate simulation models are mandatory for this optimization, because the underlying chemical processes are very slow. The simulation models themselves may be time-consuming to evaluate, hence we show how to use surrogate-model-based approaches to optimize biogas plants efficiently. In detail, a Kriging surrogate is employed. To improve model quality of this surrogate, we integrate cheaply available data into the optimization process. Doing so, Multi-fidelity modeling methods like Co-Kriging are employed. Furthermore, a two-layered modeling approach is employed to avoid deterioration of model quality due to discontinuities in the search space. At the same time, the cheaply available data is shown to be very useful for initialization of the employed optimization algorithms. Overall, we show how biogas plants can be efficiently modeled using data-driven methods, avoiding discontinuities as well as including cheaply available data. The application of the derived surrogate models to an optimization process is shown to be very difficult, yet successful for a lower problem dimension.
T3 - CIplus - 2/2014
KW - Biogas
KW - Simulation
KW - Modellierung
KW - Optimierung
KW - Kriging
KW - Biogas Plant
KW - Simulation
KW - Optimization
KW - Surrogate Models
KW - Multi-fidelity
KW - Co-Kriging
Y1 - 2014
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-644
ER -
TY - INPR
A1 - Bartz-Beielstein, Thomas
T1 - Meaningful Problem Instances and Generalizable Results
N2 - Computational intelligence methods have gained importance in several real-world domains such as process optimization, system identification, data mining, or statistical quality control. Tools are missing, which determine the applicability of computational intelligence methods in these application domains in an objective manner. Statistics provide methods for comparing algorithms on certain data sets. In the past, several test suites were presented and considered as state of the art. However, there are several drawbacks of these test suites, namely: (i) problem instances are somehow artificial and have no direct link to real-world settings; (ii) since there is a fixed number of test instances, algorithms can be fitted or tuned to this specific and very limited set of test functions; (iii) statistical tools for comparisons of several algorithms on several test problem instances are relatively complex and not easily to analyze. We propose amethodology to overcome these dificulties. It is based on standard ideas from statistics: analysis of variance and its extension to mixed models. This work combines essential ideas from two approaches: problem generation and statistical analysis of computer experiments.
T3 - CIplus - 1/2015
KW - Soft Computing
KW - Versuchsplanung
KW - Varianzanalyse
KW - Optimierung
KW - Simulation
KW - Statistische Versuchsplanung
KW - Simulationsmodell
KW - Varianzanalyse
KW - R
KW - Computational Intelligence
KW - Design of Experiments
KW - Mixed-Effects Models
KW - R
Y1 - 2015
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos-764
ER -
TY - RPRT
A1 - Zaefferer, Martin
A1 - Fischbach, Andreas
A1 - Naujoks, Boris
A1 - Bartz-Beielstein, Thomas
T1 - Simulation-based Test Functions for Optimization Algorithms
N2 - When designing or developing optimization algorithms, test functions are crucial to evaluate
performance. Often, test functions are not sufficiently difficult, diverse, flexible or relevant to real-world
applications. Previously,
test functions with real-world relevance were generated by training a machine learning model based on
real-world data. The model estimation is used as a test function.
We propose a more principled approach using simulation instead of estimation.
Thus, relevant and varied test functions
are created which represent the behavior of real-world fitness landscapes.
Importantly, estimation can lead to excessively smooth test functions
while simulation may avoid this pitfall. Moreover, the simulation
can be conditioned by the data, so that the simulation reproduces the training data
but features diverse behavior in unobserved regions of the search space.
The proposed test function generator is illustrated with an intuitive, one-dimensional
example. To demonstrate the utility of this approach it
is applied to a protein sequence optimization problem.
This application demonstrates the advantages as well as practical limits of simulation-based
test functions.
T3 - CIplus - 3/2017
KW - Optimization
KW - Test function generator
KW - Simulation
KW - Modeling
Y1 - 2017
U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:832-cos4-4777
ER -