@inproceedings{30605, author = {{Bolte, Sven and Fröhleke, Norbert and Böcker, Joachim}}, booktitle = {{2015 IEEE 3rd Workshop on Wide Bandgap Power Devices and Applications (WiPDA)}}, publisher = {{IEEE}}, title = {{{Efficiency optimization for a power factor correction (PFC) rectifier with gallium nitride transistor}}}, doi = {{10.1109/wipda.2015.7369288}}, year = {{2016}}, } @inproceedings{28483, author = {{Marín López, Andrés and Almenárez-Mendoza, Florina and Arias Cabarcos, Patricia and Díaz Sánchez, Daniel}}, booktitle = {{2016 Mediterranean Ad Hoc Networking Workshop, Med-Hoc-Net 2016, Vilanova i la Geltru, Spain, June 20-22, 2016}}, pages = {{1--8}}, publisher = {{{IEEE}}}, title = {{{Wi-Fi Direct: Lessons learned}}}, doi = {{10.1109/MedHocNet.2016.7528493}}, year = {{2016}}, } @techreport{35989, author = {{Schlegel-Matthies, Kirsten and Gigerenzer, Gerd and Wagner, Gert G.}}, issn = {{2365-919X}}, pages = {{51}}, title = {{{Digitale Welt und Gesundheit. eHealth und mHealth – Chancen und Risiken der Digitalisierung im Gesundheitsbereich}}}, year = {{2016}}, } @article{4239, abstract = {{Confocal Raman spectroscopy is applied to identify ferroelectric domain structure sensitive phonon modes in potassium titanyl phosphate. Therefore, polarization-dependent measurements in various scattering configurations have been performed to characterize the fundamental Raman spectra of the material. The obtained spectra are discussed qualitatively based on an internal mode assignment. In the main part of this work, we have characterized z-cut periodically poled potassium titanyl phosphate in terms of polarity- and structure-sensitive phonon modes. Here, we find vibrations whose intensities are linked to the ferroelectric domain walls. We interpret this in terms of changes in the polarizability originating from strain induced by domain boundaries and the inner field distribution. Hence, a direct and 3D visualization of ferroelectric domain structures becomes possible in potassium titanyl phosphate.}}, author = {{Rüsing, Michael and Eigner, Christof and Mackwitz, P. and Berth, Gerhard and Silberhorn, Christine and Zrenner, Artur}}, issn = {{0021-8979}}, journal = {{Journal of Applied Physics}}, number = {{4}}, publisher = {{AIP Publishing}}, title = {{{Identification of ferroelectric domain structure sensitive phonon modes in potassium titanyl phosphate: A fundamental study}}}, doi = {{10.1063/1.4940964}}, volume = {{119}}, year = {{2016}}, } @inproceedings{46364, abstract = {{Automated algorithm configuration procedures play an increasingly important role in the development and application of algorithms for a wide range of computationally challenging problems. Until very recently, these configuration procedures were limited to optimising a single performance objective, such as the running time or solution quality achieved by the algorithm being configured. However, in many applications there is more than one performance objective of interest. This gives rise to the multi-objective automatic algorithm configuration problem, which involves finding a Pareto set of configurations of a given target algorithm that characterises trade-offs between multiple performance objectives. In this work, we introduce MO-ParamILS, a multi-objective extension of the state-of-the-art single-objective algorithm configuration framework ParamILS, and demonstrate that it produces good results on several challenging bi-objective algorithm configuration scenarios compared to a base-line obtained from using a state-of-the-art single-objective algorithm configurator.}}, author = {{Blot, A and Hoos, H and Jourdan, L and Marmion, M and Trautmann, Heike}}, booktitle = {{LION 2016: Learning and Intelligent Optimization}}, editor = {{et al. Joaquin, Vanschooren}}, pages = {{32–47}}, publisher = {{Springer International Publishing}}, title = {{{MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework}}}, doi = {{10.1007/978-3-319-50349-3_3}}, volume = {{10079}}, year = {{2016}}, } @inbook{46363, abstract = {{The averaged Hausdorff distance has been proposed as an indicator for assessing the quality of finitely sized approximations of the Pareto front of a multiobjective problem. Since many set-based, iterative optimization algorithms store their currently best approximation in an internal archive these approximations are also termed archives. In case of two objectives and continuous variables it is known that the best approximations in terms of averaged Hausdorff distance are subsets of the Pareto front if it is concave. If it is linear or circularly concave the points of the best approximation are equally spaced. Here, it is proven that the optimal averaged Hausdorff approximation and the Pareto front have an empty intersection if the Pareto front is circularly convex. But the points of the best approximation are equally spaced and they rapidly approach the Pareto front for increasing size of the approximation.}}, author = {{Rudolph, G and Schütze, O and Trautmann, Heike}}, booktitle = {{Applications of Evolutionary Computation: 19$^th$ European Conference, EvoApplications 2016, Porto, Portugal, March 30 — April 1, 2016, Proceedings, Part II}}, editor = {{Squillero, G and Burelli, P}}, isbn = {{978-3-319-31153-1}}, pages = {{42–55}}, publisher = {{Springer International Publishing}}, title = {{{On the Closest Averaged Hausdorff Archive for a Circularly Convex Pareto Front}}}, doi = {{10.1007/978-3-319-31153-1_4}}, year = {{2016}}, } @inproceedings{46369, abstract = {{This paper formally defines multimodality in multiobjective optimization (MO). We introduce a test-bed in which multimodal MO problems with known properties can be constructed as well as numerical characteristics of the resulting landscape. Gradient- and local search based strategies are compared on exemplary problems together with specific performance indicators in the multimodal MO setting. By this means the foundation for Exploratory Landscape Analysis in MO is provided.}}, author = {{Kerschke, Pascal and Wang, Hao and Preuss, Mike and Grimme, Christian and Deutz, André and Trautmann, Heike and Emmerich, Michael}}, booktitle = {{Proceedings of the 14$^th$ International Conference on Parallel Problem Solving from Nature (PPSN XIV)}}, pages = {{962–972}}, publisher = {{Springer}}, title = {{{Towards Analyzing Multimodality of Multiobjective Landscapes}}}, doi = {{10.1007/978-3-319-45823-6_90}}, year = {{2016}}, } @inproceedings{46367, abstract = {{When selecting the best suited algorithm for an unknown optimization problem, it is useful to possess some a priori knowledge of the problem at hand. In the context of single-objective, continuous optimization problems such knowledge can be retrieved by means of Exploratory Landscape Analysis (ELA), which automatically identifies properties of a landscape, e.g., the so-called funnel structures, based on an initial sample. In this paper, we extract the relevant features (for detecting funnels) out of a large set of landscape features when only given a small initial sample consisting of 50 x D observations, where D is the number of decision space dimensions. This is already in the range of the start population sizes of many evolutionary algorithms. The new Multiple Peaks Model Generator (MPM2) is used for training the classifier, and the approach is then very successfully validated on the Black-Box Optimization Benchmark (BBOB) and a subset of the CEC 2013 niching competition problems.}}, author = {{Kerschke, Pascal and Preuss, Mike and Wessing, Simon and Trautmann, Heike}}, booktitle = {{Proceedings of the 18$^th$ Annual Conference on Genetic and Evolutionary Computation}}, isbn = {{978-1-4503-4206-3}}, pages = {{229–236}}, title = {{{Low-Budget Exploratory Landscape Analysis on Multiple Peaks Models}}}, doi = {{10.1145/2908812.2908845}}, year = {{2016}}, } @article{46371, abstract = {{One main task in evolutionary multiobjective optimization (EMO) is to obtain a suitable finite size approximation of the Pareto front which is the image of the solution set, termed the Pareto set, of a given multiobjective optimization problem. In the technical literature, the characteristic of the desired approximation is commonly expressed by closeness to the Pareto front and a sufficient spread of the solutions obtained. In this paper, we first make an effort to show by theoretical and empirical findings that the recently proposed Averaged Hausdorff (or Δ𝑝-) indicator indeed aims at fulfilling both performance criteria for bi-objective optimization problems. In the second part of this paper, standard EMO algorithms combined with a specialized archiver and a postprocessing step based on the Δ𝑝 indicator are introduced which sufficiently approximate the Δ𝑝-optimal archives and generate solutions evenly spread along the Pareto front.}}, author = {{Rudolph, G and Schütze, O and Grimme, C and Domínguez-Medina, C and Trautmann, Heike}}, journal = {{Computational Optimization and Applications (Comput. Optim. Appl.)}}, number = {{2}}, pages = {{589–618}}, title = {{{Optimal averaged Hausdorff archives for bi-objective problems: theoretical and numerical results}}}, doi = {{10.1007/s10589-015-9815-8}}, volume = {{64}}, year = {{2016}}, } @article{46372, abstract = {{We present a new hybrid evolutionary algorithm for the effective hypervolume approximation of the Pareto front of a given differentiable multi-objective optimization problem. Starting point for the local search (LS) mechanism is a new division of the decision space as we will argue that in each of these regions a different LS strategy seems to be most promising. For the LS in two out of the three regions we will utilize and adapt the Directed Search method which is capable of steering the search into any direction given in objective space and which is thus well suited for the problem at hand. We further on integrate the resulting LS mechanism into SMS-EMOA, a state-of-the-art evolutionary algorithm for hypervolume approximations. Finally, we will present some numerical results on several benchmark problems with two and three objectives indicating the strength and competitiveness of the novel hybrid.}}, author = {{Schütze, O and Sosa, Hernandez VA and Trautmann, Heike and Rudolph, G}}, journal = {{Journal of Heuristics}}, number = {{3}}, pages = {{273–300}}, title = {{{The Hypervolume based Directed Search Method for Multi-Objective Optimization Problems}}}, doi = {{10.1007/s10732-016-9310-0}}, volume = {{22}}, year = {{2016}}, } @inproceedings{46368, abstract = {{Exploratory Landscape Analysis (ELA) aims at understanding characteristics of single-objective continuous (black-box) optimization problems in an automated way. Moreover, the approach provides the basis for constructing algorithm selection models for unseen problem instances. Recently, it has gained increasing attention and numerical features have been designed by various research groups. This paper introduces the R-Package FLACCO which makes all relevant features available in a unified framework together with efficient helper functions. Moreover, a case study which gives perspectives to ELA for multi-objective optimization problems is presented.}}, author = {{Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the IEEE Congress on Evolutionary Computation (CEC)}}, title = {{{The R-Package FLACCO for Exploratory Landscape Analysis with Applications to Multi-Objective Optimization Problems}}}, doi = {{10.1109/CEC.2016.7748359}}, year = {{2016}}, } @inproceedings{11890, abstract = {{In this paper we study the influence of directional radio patterns of Bluetooth low energy (BLE) beacons on smartphone localization accuracy and beacon network planning. A two-dimensional model of the power emission characteristic is derived from measurements of the radiation pattern of BLE beacons carried out in an RF chamber. The Cramer-Rao lower bound (CRLB) for position estimation is then derived for this directional power emission model. With this lower bound on the RMS positioning error the coverage of different beacon network configurations can be evaluated. For near-optimal network planing an evolutionary optimization algorithm for finding the best beacon placement is presented.}}, author = {{Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}}, booktitle = {{24th European Signal Processing Conference (EUSIPCO 2016)}}, title = {{{Investigations into Bluetooth Low Energy Localization Precision Limits}}}, year = {{2016}}, } @inproceedings{48873, abstract = {{Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.}}, author = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Festa, Paola and Sellmann, Meinolf and Vanschoren, Joaquin}}, isbn = {{978-3-319-50349-3}}, keywords = {{Algorithm selection, Feature selection, Instance hardness, TSP}}, pages = {{48–59}}, publisher = {{Springer International Publishing}}, title = {{{Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers}}}, doi = {{10.1007/978-3-319-50349-3_4}}, year = {{2016}}, } @inproceedings{48874, abstract = {{State of the Art inexact solvers of the NP-hard Traveling Salesperson Problem TSP are known to mostly yield high-quality solutions in reasonable computation times. With the purpose of understanding different levels of instance difficulties, instances for the current State of the Art heuristic TSP solvers LKH+restart and EAX+restart are presented which are evolved using a sophisticated evolutionary algorithm. More specifically, the performance differences of the respective solvers are maximized resulting in instances which are easier to solve for one solver and much more difficult for the other. Focusing on both optimization directions, instance features are identified which characterize both types of instances and increase the understanding of solver performance differences.}}, author = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Proceedings of the XV International Conference of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence - Volume 10037}}, isbn = {{978-3-319-49129-5}}, keywords = {{Combinatorial optimization, Instance hardness, Metaheuristics, Transportation, TSP}}, pages = {{3–12}}, publisher = {{Springer-Verlag}}, title = {{{Understanding Characteristics of Evolved Instances for State-of-the-Art Inexact TSP Solvers with Maximum Performance Difference}}}, doi = {{10.1007/978-3-319-49130-1_1}}, year = {{2016}}, } @phdthesis{29027, abstract = {{Over the last years, the Linked Open Data (LOD) has evolved from a mere 12 to more than 10, 000 knowledge bases. These knowledge bases come from diverse domains including (but not limited to) publications, life sciences, social networking, government, media, linguistics. Moreover, the LOD cloud also contains a large number of crossdomain knowledge bases such as DBpedia and Yago2. These knowledge bases are commonly managed in a decentralized fashion and contain partly overlapping information. This architectural choice has led to knowledge pertaining to the same domain being published by independent entities in the LOD cloud. For example, information on drugs can be found in Diseasome as well as DBpedia and Drugbank. Furthermore, certain knowledge bases such as DBLP have been published by several bodies, which in turn has lead to duplicated content in the LOD. In addition, large amounts of geo-spatial information have been made available with the growth of heterogeneous Web of Data. The concurrent publication of knowledge bases containing related information promises to become a phenomenon of increasing importance with the growth of the number of independent data providers. Enabling the joint use of the knowledge bases published by these providers for tasks such as federated queries, cross-ontology question answering and data integration is most commonly tackled by creating links between the resources described within these knowledge bases. Within this thesis, we spur the transition from isolated knowledge bases to enriched Linked Data sets where information can be easily integrated and processed. To achieve this goal, we provide concepts, approaches and use cases that facilitate the integration and enrichment of information with other data types that are already present on the Linked Data Web with a focus on geo-spatial data. The first challenge that motivates our work is the lack of measures that use the geographic data for linking geo-spatial knowledge bases. This is partly due to the geo-spatial resources being described by the means of vector geometry. In particular, discrepancies in granularity and error measurements across knowledge bases render the selection of appropriate distance measures for geo-spatial resources difficult. We address this challenge by evaluating existing literature for pointset measures that can be used to measure the similarity of vector geometries. Then, we present and evaluate the ten measures that we derived from the literature on samples of three real knowledge bases. The second challenge we address in this thesis is the lack of automatic Link Discovery (LD) approaches capable of dealing with geospatial knowledge bases with missing and erroneous data. To this end,we present Colibri, an unsupervised approach that allows discovering links between knowledge bases while improving the quality of the instance data in these knowledge bases. A Colibri iteration begins by generating links between knowledge bases. Then, the approach makes use of these links to detect resources with probably erroneous or missing information. This erroneous or missing infor- mation detected by the approach is finally corrected or added. The third challenge we address is the lack of scalable LD approaches for tackling big geo-spatial knowledge bases. Thus, we present Deterministic Particle-Swarm Optimization (DPSO), a novel load balancing technique for LD on parallel hardware based on particle-swarm optimization. We combine this approach with the Orchid algorithm for geo-spatial linking and evaluate it on real and artificial data sets. The lack of approaches for automatic updating of links of an evolving knowledge base is our fourth challenge. This challenge is addressed in this thesis by the Wombat algorithm. Wombat is a novel approach for the discovery of links between knowledge bases that relies exclusively on positive examples. Wombat is based on generalisation via an upward refinement operator to traverse the space of Link Specifications (LS). We study the theoretical characteristics of Wombat and evaluate it on different benchmark data sets. The last challenge addressed herein is the lack of automatic approaches for geo-spatial knowledge base enrichment. Thus, we propose Deer, a supervised learning approach based on a refinement operator for enriching Resource Description Framework (RDF) data sets. We show how we can use exemplary descriptions of enriched resources to generate accurate enrichment pipelines. We evaluate our approach against manually defined enrichment pipelines and show that our approach can learn accurate pipelines even when provided with a small number of training examples. Each of the proposed approaches is implemented and evaluated against state-of-the-art approaches on real and/or artificial data sets. Moreover, all approaches are peer-reviewed and published in a con- ference or a journal paper. Throughout this thesis, we detail the ideas, implementation and the evaluation of each of the approaches. Moreover, we discuss each approach and present lessons learned. Finally, we conclude this thesis by presenting a set of possible future extensions and use cases for each of the proposed approaches.}}, author = {{Sherif, Mohamed}}, keywords = {{2016 group\_aksw sys:relevantFor:geoknow sys:relevantFor:infai sys:relevantFor:bis ngonga simba dice sherif group\_aksw geoknow deer lehmann MOLE}}, publisher = {{University of Leipzig}}, title = {{{Automating Geospatial RDF Dataset Integration and Enrichment}}}, year = {{2016}}, } @article{54172, author = {{Mousavi, Seyed Mohsen and Sadeghi, Javad and Niaki, Seyed Taghi Akhavan and Tavana, Madjid}}, issn = {{1568-4946}}, journal = {{Applied Soft Computing}}, pages = {{57--72}}, publisher = {{Elsevier BV}}, title = {{{A bi-objective inventory optimization model under inflation and discount using tuned Pareto-based algorithms: NSGA-II, NRGA, and MOPSO}}}, doi = {{10.1016/j.asoc.2016.02.014}}, volume = {{43}}, year = {{2016}}, } @article{54166, author = {{Khanjani Shiraz, Rashed and Tavana, Madjid and Di Caprio, Debora and Fukuyama, Hirofumi}}, issn = {{0022-3239}}, journal = {{Journal of Optimization Theory and Applications}}, number = {{1}}, pages = {{243--265}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Solving Geometric Programming Problems with Normal, Linear and Zigzag Uncertainty Distributions}}}, doi = {{10.1007/s10957-015-0857-y}}, volume = {{170}}, year = {{2016}}, } @article{54167, author = {{Hajipour, Vahid and Fattahi, Parviz and Tavana, Madjid and Di Caprio, Debora}}, issn = {{0307-904X}}, journal = {{Applied Mathematical Modelling}}, number = {{7-8}}, pages = {{4948--4969}}, publisher = {{Elsevier BV}}, title = {{{Multi-objective multi-layer congested facility location-allocation problem optimization with Pareto-based meta-heuristics}}}, doi = {{10.1016/j.apm.2015.12.013}}, volume = {{40}}, year = {{2016}}, } @article{54155, author = {{Tavana, Madjid and Li, Zhaojun and Mobin, Mohammadsadegh and Komaki, Mohammad and Teymourian, Ehsan}}, issn = {{0957-4174}}, journal = {{Expert Systems with Applications}}, pages = {{17--39}}, publisher = {{Elsevier BV}}, title = {{{Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS}}}, doi = {{10.1016/j.eswa.2015.11.007}}, volume = {{50}}, year = {{2016}}, } @inproceedings{46365, abstract = {{Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.}}, author = {{Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Learning and Intelligent Optimization}}, editor = {{Festa, P and Sellmann, M and Vanschoren, J}}, isbn = {{978-3-319-50348-6}}, pages = {{48–59}}, publisher = {{Springer International Publishing}}, title = {{{Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers}}}, doi = {{10.1007/978-3-319-50349-3_4}}, volume = {{10079}}, year = {{2016}}, }