Simulator adaptation at runtime for component-based simulation software

Component-based simulation software can provide many opportunities to compose and configure simulators, resulting in an algorithm selection problem for the user of this software. This thesis aims to automate the selection and adaptation of simulators at runtime in an application-independent manner. Further, it explores the potential of tailored and approximate simulators - in this thesis concretely developed for the modeling language ML-Rules - supporting the effectiveness of the adaptation scheme.Komponenten-basierte Simulationssoftware kann viele Möglichkeiten zur Komposition und Konfiguration von Simulatoren bieten und damit zu einem Konfigurationsproblem für Nutzer dieser Software führen. Das Ziel dieser Arbeit ist die Entwicklung einer generischen und automatisierten Auswahl- und Adaptionsmethode für Simulatoren. Darüber hinaus wird das Potential von spezifischen und approximativen Simulatoren anhand der Modellierungssprache ML-Rules untersucht, welche die Effektivität des entwickelten Adaptionsmechanismus erhöhen können

Reusing simulation experiments for model composition and extension

This thesis aims to reuse simulation experiments to support developing models via model reuse, with a focus on validating the resulting model. Individual models are annotated with their simulation experiments. Upon reuse of those models for building new ones, the associated simulation experiments are also reused and executed with the new model, to inspect whether the key behavior exhibited by the original models is preserved or not in the new model. Hence, the changes of model behavior resulting from the model reuse are revealed, and insights into validity of the new model are provided

Algorithm selection for power flow management

PhD ThesisAlgorithms are essential for solving many important problems, including in power systems control, where they can allow the connection of new demand and generation whilst deferring or avoiding the need for network reinforcement. However, in many problem domains no algorithm always delivers the best performance for all problems, so better performance can be achieved by using algorithm selection to select the best algorithms for each problem. This work applies algorithm selection to power systems control, with power flow management using generator curtailment examined as a representative power systems control task. The first half of this work focuses on whether potential performance benefits are available if algorithms are selected optimally for each network state. Five power flow management algorithms are implemented, which use diverse approaches such as optimal power flow, constraint satisfaction, power flow sensitivity factors, and linear programming. Four case study power systems – an 11 kV radial distribution system, a 33 kV meshed distribution system, the IEEE 14-bus system, and the IEEE 57-bus system – are used to test the algorithms over a extensive range of network states. None of the algorithms give the most effective performance for every state, in terms of minimising either the number or energy of overloads, whilst minimising curtailment. By optimally selecting algorithms for each state there are potential performance benefits for three of the four case study systems In the second half of this work, algorithm selection systems (selectors) are created in order to exploit and deliver the observed potential performance benefits of per-state algorithm selection. Existing techniques for creating algorithm selectors are adapted and extended for the power flow management application, which includes the development of a training method that allows selectors to consider two objectives simultaneously. The selectors created take measurements of network state as input and use machine learning models to make algorithm selection decisions. The models either directly predict which algorithm is likely to be the most effective, or predict the performance of each algorithm, with the algorithm with the most effective predicted performance then being selected. Both of these approaches are shown to be effective in creating algorithm selectors for power flow management that deliver statistically significant performance benefits. In some cases, the selectors are able to match the optimum performance that could be achieved by selecting between the algorithms.WSP | Parsons Brinckerhoff

Automatic Algorithm Selection for Complex Simulation Problems

To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. The thesis consists of three parts. The first part surveys existing approaches to solve the algorithm selection problem and discusses techniques to analyze simulation algorithm performance.The second part introduces a software framework for automatic simulation algorithm selection, which is evaluated in the third part.Die Auswahl des passendsten Simulationsalgorithmus für eine bestimmte Aufgabe ist oftmals schwierig. Dies liegt an der komplexen Interaktion zwischen Modelleigenschaften, Implementierungsdetails und Laufzeitumgebung. Die Arbeit ist in drei Teile gegliedert. Der erste Teil befasst sich eingehend mit Vorarbeiten zur automatischen Algorithmenauswahl, sowie mit der Leistungsanalyse von Simulationsalgorithmen. Der zweite Teil der Arbeit stellt ein Rahmenwerk zur automatischen Auswahl von Simulationsalgorithmen vor, welches dann im dritten Teil evaluiert wird

Algorithm selection in structural optimization

Thesis (Ph. D.)--Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (pages 153-162).Structural optimization is largely unused as a practical design tool, despite an extensive academic literature which demonstrates its potential to dramatically improve design processes and outcomes. Many factors inhibit optimization's application. Among them is the requirement for engineers-who generally lack the requisite expertise-to choose an optimization algorithm for a given problem. A suitable choice of algorithm improves the resulting design and reduces computational cost, yet the field of optimization does little to guide engineers in selecting from an overwhelming number of options. The goal of this dissertation is to aid, and ultimately to automate, algorithm selection, thus enhancing optimization's applicability in real-world design. The initial chapters examine the extent of the problem by reviewing relevant literature and by performing a short, empirical study of algorithm performance variation. We then specify hundreds of bridge design problems by methodically varying problem characteristics, and solve each of them with eight commonly-used nonlinear optimization algorithms. The resulting, extensive data set is used to address the algorithm selection problem. The results are first interpreted from an engineering perspective to ensure their validity as solutions to realistic problems. Algorithm performance trends are then analyzed, showing that no single algorithm outperforms the others on every problem. Those that achieve the best solutions are often computationally expensive, and those that converge quickly often arrive at poor solutions. Some problem features, such as the numbers of design variables and constraints, the structural type, and the nature of the objective function, correlate with algorithm performance. This knowledge and the generated data set are then used to develop techniques for automatic selection of optimization algorithms, based on a range supervised learning methods. Compared to a set of current, manual selection strategies, these techniques select the best algorithm almost twice as often, lead to better-quality solutions and reduced computational cost, and-on a randomly-chosen set of mass minimization problems-reduce average material use by 9.4%. The dissertation concludes by outlining future research on algorithm selection, on integrating these techniques in design software, and on adapting structural optimization to the realities of design. Keywords: Algorithm selection, structural optimization, structural design, machine learningby Rory Clune.Ph.D