@inproceedings{8760, abstract = {{n this article an efficient numerical method to solve multiobjective optimization problems for fluid flow governed by the Navier Stokes equations is presented. In order to decrease the computational effort, a reduced order model is introduced using Proper Orthogonal Decomposition and a corresponding Galerkin Projection. A global, derivative free multiobjective optimization algorithm is applied to compute the Pareto set (i.e. the set of optimal compromises) for the concurrent objectives minimization of flow field fluctuations and control cost. The method is illustrated for a 2D flow around a cylinder at Re = 100.}}, author = {{Peitz, Sebastian and Dellnitz, Michael}}, booktitle = {{PAMM}}, issn = {{1617-7061}}, pages = {{613--614}}, title = {{{Multiobjective Optimization of the Flow Around a Cylinder Using Model Order Reduction}}}, doi = {{10.1002/pamm.201510296}}, year = {{2015}}, } @inproceedings{9946, abstract = {{Intelligent mechatronic systems are able to autonomously adapt system behavior to current environmental conditions and to system states. To allow for such reactions, complex sensor and actuator systems as well as sophisticated information processing are required, making these systems increasingly complex. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability and in turn to increase reliability. The adaptation is based on switching selecting an appropriate working point at runtime. Multiple suitable working points can be found using multi-objective optimization techniques, which require an accurate system model including system reliability. At present, modeling of system reliability is a laborious manual task performed by reliability modelling experts. Despite actual system reliability being highly dependent on system dynamics, pre-existing system dynamics models and the resulting reliability model are at best loosely coupled. To allow for closer interaction among dynamics and reliability model and to ensure these are always synchronized, advanced modeling techniques are required. Therefore, an integrated model is introduced that reduces user input to a minimum and that integrates system dynamics and system reliability.}}, author = {{Kaul, Thorben and Meyer, Tobias and Sextro, Walter}}, booktitle = {{European Safety and Reliability Conference (ESREL2015)}}, editor = {{et al.}, Podofillini}}, publisher = {{Taylor and Francis}}, title = {{{Integrated Model for Dynamics and Reliability of Intelligent Mechatronic Systems}}}, doi = {{10.1201/b19094-290}}, year = {{2015}}, } @inproceedings{9954, abstract = {{To increase quality and reliability of copper wire bonds, self-optimization is a promising technique. For the implementation of self-optimization for ultrasonic heavy copper wire bonding machines, a model of stick-slip motion between tool and wire and between wire and substrate during the bonding process is essential. Investigations confirm that both of these contacts do indeed show stick-slip movement in each period oscillation. In a first step, this paper shows the importance of modeling the stick-slip effect by determining, monitoring and analyzing amplitudes and phase angles of tool tip, wire and substrate experimentally during bonding via laser measurements. In a second step, the paper presents a dynamic model which has been parameterized using an iterative numerical parameter identification method. This model includes Archard's wear approach in order to compute the lost volume of tool tip due to wear over the entire process time. A validation of the model by comparing measured and calculated amplitudes of tool tip and wire reveals high model quality. Then it is then possible to calculate the lifetime of the tool for different process parameters, i.e. values of normal force and ultrasonic voltage.}}, author = {{Unger, Andreas and Sextro, Walter and Meyer, Tobias and Eichwald, Paul and Althoff, Simon and Eacock, Florian and Brökelmann, Michael}}, booktitle = {{2015 17th Electronics Packaging Technology Conference}}, title = {{{Modeling of the Stick-Slip Effect in Heavy Copper Wire Bonding to Determine and Reduce Tool Wear}}}, doi = {{10.1109/EPTC.2015.7412375}}, year = {{2015}}, } @inproceedings{10673, author = {{Ho, Nam and Ahmed, Abdullah Fathi and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{Proc. NASA/ESA Conf. Adaptive Hardware and Systems (AHS)}}, keywords = {{cache storage, field programmable gate arrays, multiprocessing systems, parallel architectures, reconfigurable architectures, FPGA, dynamic reconfiguration, evolvable cache mapping, many-core architecture, memory-to-cache address mapping function, microarchitectural optimization, multicore architecture, nature-inspired optimization, parallelization degrees, processor, reconfigurable cache mapping, reconfigurable computing, Field programmable gate arrays, Software, Tuning}}, pages = {{1--7}}, title = {{{Microarchitectural optimization by means of reconfigurable and evolvable cache mappings}}}, doi = {{10.1109/AHS.2015.7231178}}, year = {{2015}}, } @inproceedings{10693, author = {{Kaufmann, Paul and Shen, Cong}}, booktitle = {{Genetic and Evolutionary Computation (GECCO)}}, pages = {{409--416}}, publisher = {{ACM}}, title = {{{Generator Start-up Sequences Optimization for Network Restoration Using Genetic Algorithm and Simulated Annealing}}}, year = {{2015}}, } @inproceedings{1636, author = {{Auroux, Sébastien and Draxler, Martin and Morelli, Arianna and Mancuso, Vincenzo}}, booktitle = {{2015 European Conference on Networks and Communications (EuCNC)}}, isbn = {{9781467373593}}, publisher = {{IEEE}}, title = {{{Dynamic network reconfiguration in wireless DenseNets with the CROWD SDN architecture}}}, doi = {{10.1109/eucnc.2015.7194057}}, year = {{2015}}, } @inproceedings{13222, abstract = {{When performing measurements, the effects of the measurement system itself on the measured data generally must be eliminated. Consequently, those effects, i.e. the system’s dynamic behavior, need to be known. For the piezo-composite transducers in an ultrasonic transmission line, a model based approach is used to describe their dynamic behavior and take into account its dependence on the environment temperature and the acoustic impedance of the target medium. Temperature-dependent model parameters are presented, which are obtained by performing a multiplepart identification process on the transducer model, based on electrical impedance measurements [1]. The identification process uses an inverse approach for optimizing a subset of the model parameters. Additionally, algorithmic differentiation methods are used to determine accurate derivatives. In a final optimization step, impedance measurements taken at different temperatures are used to determine the temperature dependencies of the model parameters. These can then be used to assess the plausibility of the identification results. Additionally, the parameters can be expressed as polynomials in the temperature to take different operating conditions into account.}}, author = {{Webersen, Manuel and Bause, Fabian and Rautenberg, Jens and Henning, Bernd}}, booktitle = {{AMA Conferences 2015}}, keywords = {{piezo-composite, transducer, temperature dependency, identification, plausibility}}, location = {{Nürnberg}}, pages = {{195--200}}, title = {{{Identification of temperature-dependent model parameters of ultrasonic piezo-composite transducers}}}, year = {{2015}}, } @inproceedings{10242, author = {{Szörényi, B. and Busa-Fekete, Robert and Dembczynski, K. and Hüllermeier, Eyke}}, booktitle = {{in Advances in Neural Information Processing Systems 28 (NIPS 2015)}}, pages = {{595--603}}, title = {{{Online F-Measure Optimization}}}, year = {{2015}}, } @inproceedings{29973, abstract = {{Haushaltsgeräte aus der Klasse der "Weißen Ware" tragen mit etwa einem Drittel ($34,2%$ \citeBDEW2013) zum privaten Energieverbrauch bei. Diese Veröffentlichung präsentiert eine Struktur und die dafür notwendige optimale Betriebsstrategie für Weiße Ware in einer Umgebung mit Strompreisen, die wegen der Volatilität der Regenerativen Energien stark fluktuieren. Das vorgeschlagene Konzept nutzt dafür ein dezentrales Energiemanagementsystem, das über drei Hierarchieebenen verteilt ist: die Geräteebene, die Haushaltsebene und die Ortsnetzebene. Auf der Geräteebene nutzt dieses Konzept zusätzlich Betriebsflexibilitäten der Haushaltsgeräte aus.}}, author = {{Stille, Karl Stephan Christian and Böcker, Joachim and Bettentrup, Ralf and Kaiser, Ingo}}, booktitle = {{ETG-Fachtagung "Von Smart Grids zu Smart Markets"}}, keywords = {{Energy management, hybrid energy storage system, self-optimization, multi-objective optimization, adaptive systems, pareto set, SFB614-D1, SFB614-D2, LEA-Publikation, Eigene}}, publisher = {{VDE}}, title = {{{Hierarchisches Optimierungskonzept für die Laststeuerung von Haushaltsgeräten}}}, year = {{2015}}, } @inproceedings{30603, author = {{Paradkar, Milind and Böcker, Joachim}}, booktitle = {{2015 IEEE Energy Conversion Congress and Exposition (ECCE)}}, publisher = {{IEEE}}, title = {{{3D analytical model for estimation of eddy currentlosses in the magnets of IPM machine considering the reaction field of the induced eddy currents}}}, doi = {{10.1109/ecce.2015.7310061}}, year = {{2015}}, } @misc{32999, author = {{Öhlschläger, Claudia}}, booktitle = {{Das achtzehnte Jahrhundert 40.2, 2016}}, publisher = {{Wehrhahn, zahlreiche farbige Abb.}}, title = {{{Rezension zu: Nicola Kaminski; Volker Mergenthaler: Zuschauer im Eckfenster 1821/22 oder Selbstreflexion der Journalliteratur im Journal(text). Mit einem Faksimile des Zuschauers vom April/Mai 1822}}}, year = {{2015}}, } @article{35996, author = {{Schlegel-Matthies, Kirsten}}, journal = {{Ernährung im Fokus. Zeitschrift für Fach-, Lehr- und Beratungskräfte}}, number = {{9-10}}, pages = {{256 -- 261}}, title = {{{Fleisch in unserer Gesellschaft}}}, year = {{2015}}, } @inproceedings{46375, abstract = {{In single-objective optimization different optimization strategies exist depending on the structure and characteristics of the underlying problem. In particular, the presence of so-called funnels in multimodal problems offers the possibility of applying techniques exploiting the global structure of the function. The recently proposed Exploratory Landscape Analysis approach automatically identifies problem characteristics based on a moderately small initial sample of the objective function and proved to be effective for algorithm selection problems in continuous black-box optimization. In this paper, specific features for detecting funnel structures are introduced and combined with the existing ones in order to classify optimization problems regarding the funnel property. The effectiveness of the approach is shown by experiments on specifically generated test instances and validation experiments on standard benchmark problems.}}, author = {{Kerschke, Pascal and Preuss, Mike and Wessing, Simon and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)}}, editor = {{Silva, Sara}}, isbn = {{978-1-4503-3472-3}}, pages = {{265–272}}, publisher = {{ACM}}, title = {{{Detecting Funnel Structures by Means of Exploratory Landscape Analysis}}}, doi = {{10.1145/2739480.2754642}}, year = {{2015}}, } @inproceedings{46376, abstract = {{We investigate per-instance algorithm selection techniques for solving the Travelling Salesman Problem (TSP), based on the two state-of-the-art inexact TSP solvers, LKH and EAX. Our comprehensive experiments demonstrate that the solvers exhibit complementary performance across a diverse set of instances, and the potential for improving the state of the art by selecting between them is significant. Using TSP features from the literature as well as a set of novel features, we show that we can capitalise on this potential by building an efficient selector that achieves significant performance improvements in practice. Our selectors represent a significant improvement in the state-of-the-art in inexact TSP solving, and hence in the ability to find optimal solutions (without proof of optimality) for challenging TSP instances in practice.}}, author = {{Kotthoff, Lars and Kerschke, Pascal and Hoos, Holger and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Dhaenens, Clarisse and Jourdan, Laetitia and Marmion, Marie-Eléonore}}, isbn = {{978-3-319-19084-6}}, pages = {{202–217}}, publisher = {{Springer International Publishing}}, title = {{{Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection}}}, year = {{2015}}, } @article{46379, abstract = {{In multiobjective optimization, set-based performance indicators are commonly used to assess the quality of a Pareto front approximation. Based on the scalarization obtained by these indicators, a performance comparison of multiobjective optimization algorithms becomes possible. The R2 and the Hypervolume (HV) indicator represent two recommended approaches which have shown a correlated behavior in recent empirical studies. Whereas the HV indicator has been comprehensively analyzed in the last years, almost no studies on the R2 indicator exist. In this extended version of our previous conference paper, we thus perform a comprehensive investigation of the properties of the R2 indicator in a theoretical and empirical way. The influence of the number and distribution of the weight vectors on the optimal distribution of µ solutions is analyzed. Based on a comparative analysis, specific characteristics and differences of the R2 and HV indicator are presented. Furthermore, the R2 indicator is integrated into an indicator-based steady-state evolutionary multiobjective optimization algorithm (EMOA). It is shown that the so-called R2-EMOA can accurately approximate the optimal distribution of µ solutions regarding R2.}}, author = {{Brockhoff, D and Wagner, T and Trautmann, Heike}}, journal = {{Evolutionary Computation Journal}}, number = {{3}}, pages = {{369–395}}, title = {{{R2 Indicator Based Multiobjective Search}}}, doi = {{10.1162/EVCO_a_00135}}, volume = {{23}}, year = {{2015}}, } @article{46380, abstract = {{We present methods to answer two basic questions that arise when benchmarking optimization algorithms. The first one is: which algorithm is the "best" one? and the second one is: which algorithm should I use for my real-world problem? Both are connected and neither is easy to answer. We present a theoretical framework for designing and analyzing the raw data of such benchmark experiments. This represents a first step in answering the aforementioned questions. The 2009 and 2010 BBOB benchmark results are analyzed by means of this framework and we derive insight regarding the answers to the two questions. Furthermore, we discuss how to properly aggregate rankings from algorithm evaluations on individual problems into a consensus, its theoretical background and which common pitfalls should be avoided. Finally, we address the grouping of test problems into sets with similar optimizer rankings and investigate whether these are reflected by already proposed test problem characteristics, finding that this is not always the case.}}, author = {{Mersmann, O and Preuss, M and Trautmann, Heike and Bischl, B and Weihs, C}}, journal = {{Evolutionary Computation Journal}}, number = {{1}}, pages = {{161–185}}, title = {{{Analyzing the BBOB Results by Means of Benchmarking Concepts}}}, volume = {{23}}, year = {{2015}}, } @inproceedings{48838, abstract = {{The majority of algorithms can be controlled or adjusted by parameters. Their values can substantially affect the algorithms’ performance. Since the manual exploration of the parameter space is tedious – even for few parameters – several automatic procedures for parameter tuning have been proposed. Recent approaches also take into account some characteristic properties of the problem instances, frequently termed instance features. Our contribution is the proposal of a novel concept for feature-based algorithm parameter tuning, which applies an approximating surrogate model for learning the continuous feature-parameter mapping. To accomplish this, we learn a joint model of the algorithm performance based on both the algorithm parameters and the instance features. The required data is gathered using a recently proposed acquisition function for model refinement in surrogate-based optimization: the profile expected improvement. This function provides an avenue for maximizing the information required for the feature-parameter mapping, i.e., the mapping from instance features to the corresponding optimal algorithm parameters. The approach is validated by applying the tuner to exemplary evolutionary algorithms and problems, for which theoretically grounded or heuristically determined feature-parameter mappings are available.}}, author = {{Bossek, Jakob and Bischl, Bernd and Wagner, Tobias and Rudolph, Günter}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-3472-3}}, keywords = {{evolutionary algorithms, model-based optimization, parameter tuning}}, pages = {{1319–1326}}, publisher = {{Association for Computing Machinery}}, title = {{{Learning Feature-Parameter Mappings for Parameter Tuning via the Profile Expected Improvement}}}, doi = {{10.1145/2739480.2754673}}, year = {{2015}}, } @inproceedings{48887, abstract = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}}, author = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference }}, isbn = {{978-1-4503-3472-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, online algorithms, transportation}}, pages = {{425–432}}, publisher = {{Association for Computing Machinery}}, title = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}}, doi = {{10.1145/2739480.2754705}}, year = {{2015}}, } @inproceedings{46377, abstract = {{We evaluate the performance of a multi-objective evolutionary algorithm on a class of dynamic routing problems with a single vehicle. In particular we focus on relating algorithmic performance to the most prominent characteristics of problem instances. The routing problem considers two types of customers: mandatory customers must be visited whereas optional customers do not necessarily have to be visited. Moreover, mandatory customers are known prior to the start of the tour whereas optional customers request for service at later points in time with the vehicle already being on its way. The multi-objective optimization problem then results as maximizing the number of visited customers while simultaneously minimizing total travel time. As an a-posteriori evaluation tool, the evolutionary algorithm aims at approximating the related Pareto set for specifically designed benchmarking instances differing in terms of number of customers, geographical layout, fraction of mandatory customers, and request times of optional customers. Conceptional and experimental comparisons to online heuristic procedures are provided.}}, author = {{Meisel, Stephan and Grimme, Christian and Bossek, Jakob and Wölck, Martin and Rudolph, Guenter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference (GECCO ’15)}}, isbn = {{978-1-4503-3472-3}}, pages = {{425–432}}, title = {{{Evaluation of a Multi-Objective EA on Benchmark Instances for Dynamic Routing of a Vehicle}}}, doi = {{10.1145/2739480.2754705}}, year = {{2015}}, } @phdthesis{23067, author = {{Shareef, Zeeshan}}, publisher = {{Verlagsschriftenreihe des Heinz Nixdorf Instituts, Paderborn}}, title = {{{Path Planning and Trajectory Optimization of Delta Parallel Robot}}}, volume = {{345}}, year = {{2015}}, }