@article{35137, abstract = {{Im Zentrum dieses Beitrags stehen Ergebnisse der Messung pädagogischer Kompetenzen Studierender. Dabei werden sowohl das bildungswissenschaftliche Wissen als auch die Entwicklung der Kompe­tenzselbsteinschätzungen in den Bereichen Unterrichten, Erziehen, Beurteilen und Innovieren unter Berücksichtigung individueller Voraussetzungen (Alter, Geschlecht, Abiturnote, Bachelornote, Konfession) betrachtet. Um die Ergeb­nisse hinsichtlich ihrer Bedeutung für die Professionalisierung angehender Lehrkräfte diskutieren zu können, wird, den empirischen Erkenntnissen voranstehend, die Bedeutung von Wissen für berufliches Können herausgearbeitet. Daran anschließend werden Hypothesen hergeleitet, die Anlage der Studie sowie die Testinstrumente vorgestellt, die Ergebnisse präsentiert und diskutiert. Die Ergebnisse zeigen, dass die Abitur- und Bachelornote die Varianz hinsichtlich des pädagogischen Wissens aufklären, sich eine signifikante Entwicklung der Kompetenzselbsteinschätzungen angehender Lehrkräfte feststellen lässt, aber sich angehende Religionslehrkräfte kaum von anderen Studierenden unterscheiden. Die Diskussion nimmt u. a. Rückbezug auf die Denkfiguren zur Entwicklung berufli­chen Könnens und benennt Limitationen, die mit der Studie und Kompetenzmessungen verbunden sind. Daran schließt die Formulierung eines Ausblicks an. Der Beitrag zielt insbesondere darauf, repräsentative Ergebnisse der Kompetenzmessung zu präsentieren und dabei potenzielle Einflussfaktoren auf die studentische Kompetenzent­wicklung zu beleuchten. Ein dadurch angereichertes Konglomerat belastbarer Erkenntnisse zielt darauf, langfristig zur Ableitung lehrerbildungsdidaktischer Überlegungen herangezogen werden zu können, die die studentische Professionalisierung unterstützen.}}, author = {{Caruso, Carina and Seifert, Andreas}}, issn = {{2750 - 3941}}, journal = {{Religionspädagogische Beiträge. Journal for Religion in Education }}, keywords = {{Bildungswissenschaftliches Wissen, Kompetenzmessung, Kompetenzselbsteinschätzung, Praxissemester, Professionalisierung / competence measurement, competence self-assessment, educational knowledge, internship, professionalization}}, number = {{1}}, pages = {{3--15}}, publisher = {{University of Bamberg Press}}, title = {{{Pädagogische Kompetenz als Ausgangspunkt beruflichen Könnens!? Ergebnisse der Kompetenzmessung angehender Lehrkräfte unter Berücksichtigung individueller Voraussetzungen}}}, doi = {{10.20377/rpb-101}}, volume = {{45}}, year = {{2022}}, } @inproceedings{48882, abstract = {{In multimodal multi-objective optimization (MMMOO), the focus is not solely on convergence in objective space, but rather also on explicitly ensuring diversity in decision space. We illustrate why commonly used diversity measures are not entirely appropriate for this task and propose a sophisticated basin-based evaluation (BBE) method. Also, BBE variants are developed, capturing the anytime behavior of algorithms. The set of BBE measures is tested by means of an algorithm configuration study. We show that these new measures also transfer properties of the well-established hypervolume (HV) indicator to the domain of MMMOO, thus also accounting for objective space convergence. Moreover, we advance MMMOO research by providing insights into the multimodal performance of the considered algorithms. Specifically, algorithms exploiting local structures are shown to outperform classical evolutionary multi-objective optimizers regarding the BBE variants and respective trade-off with HV.}}, author = {{Heins, Jonathan and Rook, Jeroen and Schäpermeier, Lennart and Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Parallel Problem Solving from Nature (PPSN XVII)}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tusar, Tea}}, isbn = {{978-3-031-14714-2}}, keywords = {{Anytime behavior, Benchmarking, Continuous optimization, Multi-objective optimization, Multimodality, Performance metric}}, pages = {{192–206}}, publisher = {{Springer International Publishing}}, title = {{{BBE: Basin-Based Evaluation of Multimodal Multi-objective Optimization Problems}}}, doi = {{10.1007/978-3-031-14714-2_14}}, year = {{2022}}, } @inproceedings{48896, abstract = {{Hardness of Multi-Objective (MO) continuous optimization problems results from an interplay of various problem characteristics, e. g. the degree of multi-modality. We present a benchmark study of classical and diversity focused optimizers on multi-modal MO problems based on automated algorithm configuration. We show the large effect of the latter and investigate the trade-off between convergence in objective space and diversity in decision space.}}, author = {{Rook, Jeroen and Trautmann, Heike and Bossek, Jakob and Grimme, Christian}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-9268-6}}, keywords = {{configuration, multi-modality, multi-objective optimization}}, pages = {{356–359}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems}}}, doi = {{10.1145/3520304.3528998}}, year = {{2022}}, } @article{35206, author = {{Bonnard, Bernard and Rouot, Jérémy and Wembe Moafo, Boris Edgar}}, issn = {{2156-8472}}, journal = {{Mathematical Control and Related Fields}}, keywords = {{Applied Mathematics, Control and Optimization, General Medicine}}, pages = {{0--0}}, publisher = {{American Institute of Mathematical Sciences (AIMS)}}, title = {{{Accessibility properties of abnormal geodesics in optimal control illustrated by two case studies}}}, doi = {{10.3934/mcrf.2022052}}, year = {{2022}}, } @inproceedings{40046, abstract = {{Theoretical approaches to the transformation towards an inclusive educational system in Germany mostly agree on the involvement of developmental tasks in subject related research (Hinz, 2011). The common understanding of inclusion as a process geared towards equal participation of all children (Booth, 2012) requires a reflexive questioning of established values, attitudes and practices in order to develop inclusive subject related research, teacher training and teaching and learning (Pech & Schomaker, 2013). Among other things, this results in consequences for the design of pre- service teacher training. To a large extent, teacher education is driven by the promotion of central competencies, interests and self-efficacy (Baumert & Kunter, 2011). It aims towards the development and realisation of inclusive interdisciplinary science and social studies (‘Sachunterricht’) in primary education (Moser, 2018). In conjunction with largely acknowledged constructivist approaches to teaching and learning (Möller, 2001), the development of personality, the consideration of basic needs (Deci & Ryan, 1993) and promotion of individual potentials are repeatedly fundamentally represented in subject related and pedagogical considerations (Feuser, 1989; GDSU, 2013). Therefore, the aforementioned constructivist approach is connected to several certain key paradigms for teaching and learning processes (e.g., Vygotskij, 1978; Posner et al., 1982; van de Pol et al., 2010). In this regard, the nature of primary school students’ basic needs have empirically not been sufficiently studied yet. Theoretical frameworks from motivational psychology (Deci & Ryan, 1993) do not explicitly address how individual needs differ and how the diversity of needs can be included in joint-learning, multi-perspective technology education classes. The research project the present paper is part of aims to develop a research-based concept for the professionalisation of pre-service teachers in a seminar course. Therefore, the promotion of the pre- service teachers’ interests and self-efficacy expectations have been assessed in a pre-post research design with a control group visiting another course not related to technology education and inclusion. The present paper describes and discusses first results of the project and will give an outlook on subsequent developmental tasks.}}, author = {{Schröer, Franz and Tenberge, Claudia}}, booktitle = {{PATT39 - PATT on the Edge Technology, Innovation and Education}}, editor = {{Gill, David and Tuff, Jim and Kennedy, Thomas and Pendergast, Shawn and Jamil, Sana}}, keywords = {{Inclusion, basic needs, pre-service teacher training, interest, self-efficacy}}, location = {{St. John’s, Newfoundland and Labrador, Canada}}, pages = {{49--57}}, title = {{{How to enable pre-service teachers to design technological teaching and learning inclusively? – On the nature and consideration of basic needs in teacher training}}}, year = {{2022}}, } @article{36083, author = {{Constantiou, Ioanna and Mukkamala, Alivelu and Sjöklint, Mimmi and Trier, Matthias}}, issn = {{0960-085X}}, journal = {{European Journal of Information Systems}}, keywords = {{Library and Information Sciences, Information Systems, Self-Tracking, User Behaviour, Discontinuance}}, pages = {{1--21}}, publisher = {{Informa UK Limited}}, title = {{{Engaging with self-tracking applications: how do users respond to their performance data?}}}, doi = {{10.1080/0960085x.2022.2081096}}, year = {{2022}}, } @article{47961, abstract = {{Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.}}, author = {{Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}}, issn = {{1996-1073}}, journal = {{Energies}}, keywords = {{Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous), Building and Construction}}, number = {{14}}, publisher = {{MDPI AG}}, title = {{{Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping}}}, doi = {{10.3390/en15145215}}, volume = {{15}}, year = {{2022}}, } @inproceedings{31066, abstract = {{While trade-offs between modeling effort and model accuracy remain a major concern with system identification, resorting to data-driven methods often leads to a complete disregard for physical plausibility. To address this issue, we propose a physics-guided hybrid approach for modeling non-autonomous systems under control. Starting from a traditional physics-based model, this is extended by a recurrent neural network and trained using a sophisticated multi-objective strategy yielding physically plausible models. While purely data-driven methods fail to produce satisfying results, experiments conducted on real data reveal substantial accuracy improvements by our approach compared to a physics-based model. }}, author = {{Schön, Oliver and Götte, Ricarda-Samantha and Timmermann, Julia}}, booktitle = {{14th IFAC Workshop on Adaptive and Learning Control Systems (ALCOS 2022)}}, keywords = {{neural networks, physics-guided, data-driven, multi-objective optimization, system identification, machine learning, dynamical systems}}, location = {{Casablanca, Morocco}}, number = {{12}}, pages = {{19--24}}, title = {{{Multi-Objective Physics-Guided Recurrent Neural Networks for Identifying Non-Autonomous Dynamical Systems}}}, doi = {{https://doi.org/10.1016/j.ifacol.2022.07.282}}, volume = {{55}}, year = {{2022}}, } @inproceedings{29803, abstract = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of bonding: the constraint is used to check one quality feature of the process and the use of batches leads to more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding process that provides very high quality bonds without using a physical model. We also show that the quality of the Bayesian optimization based control outperforms random search as well as manual search by a user. Using a simple prior knowledge model derived from data further improves the quality of the connection. The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary, Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward control without full modeling of the underlying physical processes.}}, author = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}}, booktitle = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}}, isbn = {{978-989-758-549-4}}, keywords = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}}, location = {{Online}}, pages = {{383--394}}, title = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}}, year = {{2022}}, } @article{34614, abstract = {{Mit steigenden Optimierungsanforderungen an das Individuum wächst auch das indivi- duelle Bedürfnis nach Kontrolle. Dieses kann u. a. durch self tracking-Technologien erfüllt werden. Anhand von drei Fallbeispielen – der Personenwaage, dem Wearable und dem habit tracker – zeigt dieser Aufsatz, wie sich medienbasierte Selbsttechnologien im historischen Verlauf intensiviert und stärker in den Alltag integriert haben. Ein besonderer Fokus liegt dabei auf der Ambivalenz dieser Medien: Ermöglichen sie auf der einen Seite zwar eine Selbstkontrolle und stellen so potenziell sta- bilisierende Ressourcen für das Individuum dar, schaffen sie auf der anderen Seite auch neue Anforderungen, die es zu erfüllen gilt.}}, author = {{Schloots, Franziska Margarete}}, journal = {{ffk Journal}}, keywords = {{self-tracking, Selbsttechnologien, Wearable, Bullet Journal, Personenwaage, Selbstvermessung}}, number = {{7}}, pages = {{74--91}}, title = {{{‚Understand what’s happening within‘. Selbstkontrolle mit Personenwaage, Wearable und habit tracker}}}, doi = {{10.25969/MEDIAREP/18238}}, volume = {{6}}, year = {{2022}}, } @article{21004, abstract = {{Automated machine learning (AutoML) supports the algorithmic construction and data-specific customization of machine learning pipelines, including the selection, combination, and parametrization of machine learning algorithms as main constituents. Generally speaking, AutoML approaches comprise two major components: a search space model and an optimizer for traversing the space. Recent approaches have shown impressive results in the realm of supervised learning, most notably (single-label) classification (SLC). Moreover, first attempts at extending these approaches towards multi-label classification (MLC) have been made. While the space of candidate pipelines is already huge in SLC, the complexity of the search space is raised to an even higher power in MLC. One may wonder, therefore, whether and to what extent optimizers established for SLC can scale to this increased complexity, and how they compare to each other. This paper makes the following contributions: First, we survey existing approaches to AutoML for MLC. Second, we augment these approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking framework that supports a fair and systematic comparison. Fourth, we conduct an extensive experimental study, evaluating the methods on a suite of MLC problems. We find a grammar-based best-first search to compare favorably to other optimizers.}}, author = {{Wever, Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke}}, issn = {{0162-8828}}, journal = {{IEEE Transactions on Pattern Analysis and Machine Intelligence}}, keywords = {{Automated Machine Learning, Multi Label Classification, Hierarchical Planning, Bayesian Optimization}}, pages = {{1--1}}, title = {{{AutoML for Multi-Label Classification: Overview and Empirical Evaluation}}}, doi = {{10.1109/tpami.2021.3051276}}, year = {{2021}}, } @article{21808, abstract = {{Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.}}, author = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}}, journal = {{Transactions on Network and Service Management}}, keywords = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}}, publisher = {{IEEE}}, title = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}}, doi = {{10.1109/TNSM.2021.3076503}}, year = {{2021}}, } @techreport{33854, abstract = {{Macrodiversity is a key technique to increase the capacity of mobile networks. It can be realized using coordinated multipoint (CoMP), simultaneously connecting users to multiple overlapping cells. Selecting which users to serve by how many and which cells is NP-hard but needs to happen continuously in real time as users move and channel state changes. Existing approaches often require strict assumptions about or perfect knowledge of the underlying radio system, its resource allocation scheme, or user movements, none of which is readily available in practice. Instead, we propose three novel self-learning and self-adapting approaches using model-free deep reinforcement learning (DRL): DeepCoMP, DD-CoMP, and D3-CoMP. DeepCoMP leverages central observations and control of all users to select cells almost optimally. DD-CoMP and D3-CoMP use multi-agent DRL, which allows distributed, robust, and highly scalable coordination. All three approaches learn from experience and self-adapt to varying scenarios, reaching 2x higher Quality of Experience than other approaches. They have very few built-in assumptions and do not need prior system knowledge, making them more robust to change and better applicable in practice than existing approaches.}}, author = {{Schneider, Stefan Balthasar and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, keywords = {{mobility management, coordinated multipoint, CoMP, cell selection, resource management, reinforcement learning, multi agent, MARL, self-learning, self-adaptation, QoE}}, title = {{{DeepCoMP: Coordinated Multipoint Using Multi-Agent Deep Reinforcement Learning}}}, year = {{2021}}, } @article{46318, abstract = {{Multi-objective (MO) optimization, i.e., the simultaneous optimization of multiple conflicting objectives, is gaining more and more attention in various research areas, such as evolutionary computation, machine learning (e.g., (hyper-)parameter optimization), or logistics (e.g., vehicle routing). Many works in this domain mention the structural problem property of multimodality as a challenge from two classical perspectives: (1) finding all globally optimal solution sets, and (2) avoiding to get trapped in local optima. Interestingly, these streams seem to transfer many traditional concepts of single-objective (SO) optimization into claims, assumptions, or even terminology regarding the MO domain, but mostly neglect the understanding of the structural properties as well as the algorithmic search behavior on a problem’s landscape. However, some recent works counteract this trend, by investigating the fundamentals and characteristics of MO problems using new visualization techniques and gaining surprising insights. Using these visual insights, this work proposes a step towards a unified terminology to capture multimodality and locality in a broader way than it is usually done. This enables us to investigate current research activities in multimodal continuous MO optimization and to highlight new implications and promising research directions for the design of benchmark suites, the discovery of MO landscape features, the development of new MO (or even SO) optimization algorithms, and performance indicators. For all these topics, we provide a review of ideas and methods but also an outlook on future challenges, research potential and perspectives that result from recent developments.}}, author = {{Grimme, Christian and Kerschke, Pascal and Aspar, Pelin and Trautmann, Heike and Preuss, Mike and Deutz, André H. and Wang, Hao and Emmerich, Michael}}, issn = {{0305-0548}}, journal = {{Computers & Operations Research}}, keywords = {{Multimodal optimization, Multi-objective continuous optimization, Landscape analysis, Visualization, Benchmarking, Theory, Algorithms}}, pages = {{105489}}, title = {{{Peeking beyond peaks: Challenges and research potentials of continuous multimodal multi-objective optimization}}}, doi = {{https://doi.org/10.1016/j.cor.2021.105489}}, volume = {{136}}, year = {{2021}}, } @inproceedings{48853, abstract = {{In practise, it is often desirable to provide the decision-maker with a rich set of diverse solutions of decent quality instead of just a single solution. In this paper we study evolutionary diversity optimization for the knapsack problem (KP). Our goal is to evolve a population of solutions that all have a profit of at least (1 - {$ϵ$}) {$\cdot$} OPT, where OPT is the value of an optimal solution. Furthermore, they should differ in structure with respect to an entropy-based diversity measure. To this end we propose a simple ({$\mu$} + 1)-EA with initial approximate solutions calculated by a well-known FPTAS for the KP. We investigate the effect of different standard mutation operators and introduce biased mutation and crossover which puts strong probability on flipping bits of low and/or high frequency within the population. An experimental study on different instances and settings shows that the proposed mutation operators in most cases perform slightly inferior in the long term, but show strong benefits if the number of function evaluations is severely limited.}}, author = {{Bossek, Jakob and Neumann, Aneta and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, knapsack problem, tailored operators}}, pages = {{556–564}}, publisher = {{Association for Computing Machinery}}, title = {{{Breeding Diverse Packings for the Knapsack Problem by Means of Diversity-Tailored Evolutionary Algorithms}}}, doi = {{10.1145/3449639.3459364}}, year = {{2021}}, } @inproceedings{48860, abstract = {{In the area of evolutionary computation the calculation of diverse sets of high-quality solutions to a given optimization problem has gained momentum in recent years under the term evolutionary diversity optimization. Theoretical insights into the working principles of baseline evolutionary algorithms for diversity optimization are still rare. In this paper we study the well-known Minimum Spanning Tree problem (MST) in the context of diversity optimization where population diversity is measured by the sum of pairwise edge overlaps. Theoretical results provide insights into the fitness landscape of the MST diversity optimization problem pointing out that even for a population of {$\mu$} = 2 fitness plateaus (of constant length) can be reached, but nevertheless diverse sets can be calculated in polynomial time. We supplement our theoretical results with a series of experiments for the unconstrained and constraint case where all solutions need to fulfill a minimal quality threshold. Our results show that a simple ({$\mu$} + 1)-EA can effectively compute a diversified population of spanning trees of high quality.}}, author = {{Bossek, Jakob and Neumann, Frank}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-8350-9}}, keywords = {{evolutionary algorithms, evolutionary diversity optimization, minimum spanning tree, runtime analysis}}, pages = {{198–206}}, publisher = {{Association for Computing Machinery}}, title = {{{Evolutionary Diversity Optimization and the Minimum Spanning Tree Problem}}}, doi = {{10.1145/3449639.3459363}}, year = {{2021}}, } @article{48854, abstract = {{We contribute to the theoretical understanding of randomized search heuristics for dynamic problems. We consider the classical vertex coloring problem on graphs and investigate the dynamic setting where edges are added to the current graph. We then analyze the expected time for randomized search heuristics to recompute high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a setting where the number of colors is bounded and we are minimizing the number of conflicts. Iterated local search algorithms use an unbounded color palette and aim to use the smallest colors and, consequently, the smallest number of colors. We identify classes of bipartite graphs where reoptimization is as hard as or even harder than optimization from scratch, i.e., starting with a random initialization. Even adding a single edge can lead to hard symmetry problems. However, graph classes that are hard for one algorithm turn out to be easy for others. In most cases our bounds show that reoptimization is faster than optimizing from scratch. We further show that tailoring mutation operators to parts of the graph where changes have occurred can significantly reduce the expected reoptimization time. In most settings the expected reoptimization time for such tailored algorithms is linear in the number of added edges. However, tailored algorithms cannot prevent exponential times in settings where the original algorithm is inefficient.}}, author = {{Bossek, Jakob and Neumann, Frank and Peng, Pan and Sudholt, Dirk}}, issn = {{0178-4617}}, journal = {{Algorithmica}}, keywords = {{Dynamic optimization, Evolutionary algorithms, Running time analysis}}, number = {{10}}, pages = {{3148–3179}}, title = {{{Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}}}, doi = {{10.1007/s00453-021-00838-3}}, volume = {{83}}, year = {{2021}}, } @article{21436, abstract = {{Ultrasonic wire bonding is a solid-state joining process, used in the electronics industry to form electrical connections, e.g. to connect electrical terminals within semiconductor modules. Many process parameters affect the bond strength, such like the bond normal force, ultrasonic power, wire material and bonding frequency. Today, process design, development, and optimization is most likely based on the knowledge of process engineers and is mainly performed by experimental testing. In this contribution, a newly developed simulation tool is presented, to reduce time and costs and efficiently determine optimized process parameter. Based on a co-simulation of MATLAB and ANSYS, the different physical phenomena of the wire bonding process are considered using finite element simulation for the complex plastic deformation of the wire and reduced order models for the transient dynamics of the transducer, wire, substrate and bond formation. The model parameters such as the coefficients of friction between bond tool and wire and between wire and substrate were determined for aluminium and copper wire in experiments with a test rig specially developed for the requirements of heavy wire bonding. To reduce simulation time, for the finite element simulation a restart analysis and high performance computing is utilized. Detailed analysis of the bond formation showed, that the normal pressure distribution in the contact between wire and substrate has high impact on bond formation and distribution of welded areas in the contact area.}}, author = {{Schemmel, Reinhard and Krieger, Viktor and Hemsel, Tobias and Sextro, Walter}}, issn = {{0026-2714}}, journal = {{Microelectronics Reliability}}, keywords = {{Ultrasonic heavy wire bonding, Co-simulation, ANSYS, MATLAB, Process optimization, Friction coefficient, Copper-copper, Aluminium-copper}}, pages = {{114077}}, title = {{{Co-simulation of MATLAB and ANSYS for ultrasonic wire bonding process optimization}}}, doi = {{https://doi.org/10.1016/j.microrel.2021.114077}}, volume = {{119}}, year = {{2021}}, } @inbook{22930, abstract = {{Self-piercing riveting is an established technique for joining multi-material structures in car body manufacturing. Rivets for self-piercing riveting differ in their geometry, the material used, the condition of the material and their surface condition. To shorten the manufacturing process by omitting the heat treatment and the coating process, the authors have elaborated a concept for the use of stainless steel with high strain hardening as a rivet material. The focus of the present investigation is on the evaluation of the influences of the rivet’s geometry and material on its deformation behaviour. Conventional rivets of types P and HD2, a rivet with an improved geometry made of treatable steel 38B2, and rivets made of the stainless steels 1.3815 and 1.4541 are examined. The analysis is conducted by means of multi-step joining tests for two material combinations comprising high-strength steel HCT70X and aluminium EN AW-5083. The joints are cut to provide a cross-section and the deformation behaviour of the different rivets is analysed on the basis of the measured changes in geometry and hardness. In parallel, an examination of the force-stroke curves provides further insights. It can be demonstrated that, besides the geometry, the material strength, in particular, has a significant influence on the deformation behaviour of the rivet. The strength of steel 1.4541 is seen to be too low for the joining task, while the strength of steel 1.3815 is sufficient, and hence the investigation confirms the capability of rivets made of 1.3815 for joining even challenging material combinations.}}, author = {{Uhe, Benedikt and Kuball, Clara-Maria and Merklein, Marion and Meschut, Gerson}}, booktitle = {{Forming the Future - Proceedings of the 13th International Conference on the Technology of Plasticity. The Minerals, Metals & Materials Series.}}, editor = {{Daehn, Glenn and Cao, Jian and Kinsey, Brad and Tekkaya, Erman and Vivek, Anupam and Yoshida, Yoshinori}}, keywords = {{Self-piercing riveting, Lightweight design, Deformation behaviour, Stainless steel, High nitrogen steel}}, pages = {{1495--1506}}, publisher = {{Springer}}, title = {{{Self-Piercing Riveting Using Rivets Made of Stainless Steel with High Strain Hardening}}}, doi = {{10.1007/978-3-030-75381-8_124}}, year = {{2021}}, } @inproceedings{22274, abstract = {{The use of high-strength steel and aluminium is rising due to the intensified efforts being made in lightweight design, and self-piercing riveting is becoming increasingly important. Conventional rivets for self-piercing riveting differ in their geometry, the material used, the condition of the material and the coating. To shorten the manufacturing process, the use of stainless steel with high strain hardening as the rivet material represents a promising approach. This allows the coating of the rivets to be omitted due to the corrosion resistance of the material and, since the strength of the stainless steel is achieved by cold forming, heat treatment is no longer required. In addition, it is possible to adjust the local strength within the rivet. Because of that, the authors have elaborated a concept for using high nitrogen steel 1.3815 as the rivet material. The present investigation focusses on the joint strength in order to evaluate the capability of rivets in high nitrogen steel by comparison to conventional rivets made of treatable steel. Due to certain challenges in the forming process of the high nitrogen steel rivets, deviations result from the targeted rivet geometry. Mainly these deviations cause a lower joint strength with these rivets, which is, however, adequate. All in all, the capability of the new rivet is proven by the results of this investigation. }}, author = {{Uhe, Benedikt and Kuball, Clara-Maria and Merklein, Marion and Meschut, Gerson}}, keywords = {{Self-piercing Riveting, Joining Technology, Rivet Geometry, Rivet Material, High Nitrogen Steel, Joint Strength}}, location = {{Liège, Belgien}}, title = {{{Strength of self-piercing riveted Joints with conventional Rivets and Rivets made of High Nitrogen Steel}}}, doi = {{10.25518/esaform21.1911}}, year = {{2021}}, }