Refine
Year of publication
Document Type
- Report (22) (remove)
Has Fulltext
- yes (22)
Keywords
- Optimierung (10)
- Soft Computing (8)
- Modellierung (6)
- Evolutionärer Algorithmus (4)
- Globale Optimierung (4)
- Genetisches Programmieren (3)
- Maschinelles Lernen (3)
- Mehrkriterielle Optimierung (3)
- Metaheuristik (3)
- Optimization (3)
- Sequential Parameter Optimization (3)
- Sequentielle Parameter Optimierung (3)
- Computational Intelligence (2)
- Ensemble Methods (2)
- Evolutionsstrategie (2)
- Genetic Programming (2)
- Genetische Algorithmen (2)
- Multi-Criteria Optimization (2)
- Multiobjective Optimization (2)
- Optimierungsproblem (2)
- Prognose (2)
- Simulation (2)
- Surrogat-Modellierung (2)
- Surrogate Modeling (2)
- Surrogate Models (2)
- Versuchsplanung (2)
- Algorithm Tuning (1)
- Algorithmus (1)
- Automated Learning (1)
- Bayesian Learning (1)
- Bayesian Regression (1)
- Co-Kriging (1)
- Cyber-physische Produktionssysteme (1)
- Cyclone Dust Separator (1)
- Data Analysis (1)
- Data Mining (1)
- Data Modelling (1)
- Datenanalyse (1)
- Ensemble based modeling (1)
- Entstauber (1)
- Event Detection (1)
- Evolution Strategies (1)
- Evolutionary Algorithms (1)
- Evolutionsstrategien (1)
- Evolutionäre Algorithmen (1)
- Expected Improvement (1)
- Experiment (1)
- Experimental Algorithmics (1)
- Fehlende Daten (1)
- Finanzwirtschaft (1)
- Flushing (1)
- Function Approximation (1)
- Gaussian Process (1)
- Genetic Algorithms (1)
- Genetic programming (1)
- Heuristics (1)
- Imputation (1)
- Kognitive Referenzarchitektur (1)
- Kriging (1)
- Lineare Regression (1)
- Machine Learning (1)
- Massive Online Analysis (1)
- Metamodel (1)
- Metamodell (1)
- Missing Data (1)
- Mixed Models (1)
- Model Selection (1)
- Multi-criteria Optimization (1)
- On-line Algorithm (1)
- Parametertuning (1)
- R (1)
- Regression (1)
- SPOT (1)
- Sensortechnik (1)
- Simulated annealing (1)
- Spülen (1)
- Stacked Generalization (1)
- Statistics (1)
- Surrogate (1)
- Surrogate Model (1)
- Surrogate Optimization (1)
- Surrogate-Model-Based Optimization (1)
- Surrogate-model-based Optimization (1)
- Surrogatmodellbasierte Optimierung (1)
- Taxonomie (1)
- Taxonomy (1)
- Time-series (1)
- Trinkwasserversorgung (1)
- Unsicherheit (1)
- Vorverarbeitung (1)
- Wasserwirtschaft (1)
- Water Distribution Systems (1)
- Water Quality Monitoring (1)
- Zeitreihe (1)
- Zeitreihenanalyse (1)
- Zylon Enstauber (1)
This paper introduces UniFIeD, a new data preprocessing method for time series. UniFIeD can cope with large intervals of missing data. A scalable test function generator, which allows the simulation of time series with different gap sizes, is presented additionally. An experimental study demonstrates that (i) UniFIeD shows a significant better performance than simple imputation methods and (ii) UniFIeD is able to handle situations, where advanced imputation methods fail. The results are independent from the underlying error measurements.
Drinking water supply and distribution systems are critical infrastructure that has to be well maintained for the safety of the public. One important tool in the maintenance of water distribution systems (WDS) is flushing. Flushing is a process carried out in a periodic fashion to clean sediments and other contaminants in the water pipes. Given the different topographies, water composition and supply demand between WDS no single flushing strategy is suitable for all of them. In this report a non-exhaustive overview of optimization methods for flushing in WDS is given. Implementation of optimization methods for the flushing procedure and the flushing planing are presented. Suggestions are given as a possible option to optimise existing flushing planing frameworks.
This report presents a practical approach to stacked generalization in surrogate model based optimization. It exemplifies the integration of stacking methods into the surrogate model building process. First, a brief overview of the current state in surrogate model based opti- mization is presented. Stacked generalization is introduced as a promising ensemble surrogate modeling approach. Then two examples (the first is based on a real world application and the second on a set of artificial test functions) are presented. These examples clearly illustrate two properties of stacked generalization: (i) combining information from two poor performing models can result in a good performing model and (ii) even if the ensemble contains a good performing model, combining its information with information from poor performing models results in a relatively small performance decrease only.
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.
Sequential Parameter Optimization is a model-based optimization methodology, which includes several techniques for handling uncertainty. Simple approaches such as sharp- ening and more sophisticated approaches such as optimal computing budget allocation are available. For many real world engineering problems, the objective function can be evaluated at different levels of fidelity. For instance, a CFD simulation might provide a very time consuming but accurate way to estimate the quality of a solution.The same solution could be evaluated based on simplified mathematical equations, leading to a cheaper but less accurate estimate. Combining these different levels of fidelity in a model-based optimization process is referred to as multi-fidelity optimization. This chapter describes uncertainty-handling techniques for meta-model based search heuristics in combination with multi-fidelity optimization. Co-Kriging is one power- ful method to correlate multiple sets of data from different levels of fidelity. For the first time, Sequential Parameter Optimization with co-Kriging is applied to noisy test functions. This study will introduce these techniques and discuss how they can be applied to real-world examples.
Evolutionary algorithm (EA) is an umbrella term used to describe population-based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming. On the basis of the evolutionary cycle, similarities and differences between these algorithms are described. We briefly discuss how EAs can be adapted to work well in case of multiple objectives, and dynamic or noisy optimization problems. We look at the tuning of algorithms and present some recent developments coming from theory. Finally, typical applications of EAs to real-world problems are shown, with special emphasis on data-mining applications
Modelling Zero-inflated Rainfall Data through the Use of Gaussian Process and Bayesian Regression
(2018)
Rainfall is a key parameter for understanding the water cycle. An accurate rainfall measurement is vital in the development of hydrological models. By means of indirect measurement, satellites can nowadays estimate the rainfall around the world. However, these measurements are not always accurate. As a first approach to generate a bias-corrected rainfall estimate using satellite data, the performance of Gaussian process and Bayesian regression is studied. The results show Gaussian process as the better option for this dataset but leave place to improvements on both modelling strategies.
In this paper we present a comparison of different data driven modeling methods. The first instance of a data driven linear Bayesian model is compared with several linear regression models, a Kriging model and a genetic programming model.
The models are build on industrial data for the development of a robust gas sensor.
The data contain limited amount of samples and a high variance.
The mean square error of the models implemented in a test dataset is used as the comparison strategy.
The results indicate that standard linear regression approaches as well as Kriging and GP show good results,
whereas the Bayesian approach, despite the fact that it requires additional resources, does not lead to improved results.
Formerly, multi-criteria optimization algorithms were often tested using tens of thousands function evaluations. In many real-world settings function evaluations are very costly or the available budget is very limited. Several methods were developed to solve these cost-extensive multi-criteria optimization problems by reducing the number of function evaluations by means of surrogate optimization. In this study, we apply different multi-criteria surrogate optimization methods to improve (tune) an event-detection software for water-quality monitoring. For tuning two important parameters of this software, four state-of-the-art methods are compared: S-Metric-Selection Efficient Global Optimization (SMS-EGO), S-Metric-Expected Improvement for Efficient Global Optimization SExI-EGO, Euclidean Distance based Expected Improvement Euclid-EI (here referred to as MEI-SPOT due to its implementation in the Sequential Parameter Optimization Toolbox SPOT) and a multi-criteria approach based on SPO (MSPOT). Analyzing the performance of the different methods provides insight into the working-mechanisms of cutting-edge multi-criteria solvers. As one of the approaches, namely MSPOT, does not consider the prediction variance of the surrogate model, it is of interest whether this can lead to premature convergence on the practical tuning problem. Furthermore, all four approaches will be compared to a simple SMS-EMOA to validate that the use of surrogate models is justified on this problem.
Ziel des Forschungsprojektes "Mehrkriterielle CI-basierte Optimierungsverfahren für den industriellen Einsatz" (MCIOP) war die Verringerung von Schadstoffemissionen in Kohlekraftwerken. Der wissenschaftliche Fokus lag auf der Entwicklung von Methoden, die in der Lage sind, interpretierbare Modelle für die Schadstoffemissionen automatisch zu generieren. Hierzu wurden mehrkriterielle Optimierungsverfahren entwickelt und eingesetzt. Zur Zeit- und Kostenreduktion wurde die Optimierung durch Surrogat-Modelle erfolgen, die abgestuft mit aufwändigeren Simulationen zum Einsatz kamen („optimization via simulation“). Bei der Untersuchung von Staubabscheidern konnten durch eine mehrkriterielle Optimierung unterschiedliche Zielgrößen, wie z.B. Abscheidegrad und Druckverlust, gleichzeitig berücksichtigt werden.
Dieser Bericht beschreibt die im Projekt MCIOP im Zeitraum von August 2011 bis einschließlich Juni 2015 erzielten Ergebnisse.