@inproceedings{5675, abstract = {{When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system which we develop in joint cooperation with a disaster management authority and a software development company.}}, author = {{Rauchecker, Gerhard and Schryen, Guido}}, booktitle = {{Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management}}, keywords = {{Coordination of spontaneous volunteers, volunteer coordination system, decision support, scheduling optimization model, linear programming}}, location = {{Rochester, NY, USA}}, title = {{{Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief}}}, year = {{2018}}, } @inproceedings{9999, abstract = {{Ultrasonic wire bonding is an indispensable process in the industrial manufacturing of semiconductor devices. Copper wire is increasingly replacing the well-established aluminium wire because of its superior electrical, thermal and mechanical properties. Copper wire processes differ significantly from aluminium processes and are more sensitive to disturbances, which reduces the range of parameter values suitable for a stable process. Disturbances can be compensated by an adaption of process parameters, but finding suitable parameters manually is difficult and time-consuming. This paper presents a physical model of the ultrasonic wire bonding process including the friction contact between tool and wire. This model yields novel insights into the process. A prototype of a multi-objective optimizing bonding machine (MOBM) is presented. It uses multi-objective optimization, based on the complete process model, to automatically select the best operating point as a compromise of concurrent objectives.}}, author = {{Unger, Andreas and Hunstig, Matthias and Meyer, Tobias and Brökelmann, Michael and Sextro, Walter}}, booktitle = {{In Proceedings of IMAPS 2018 – 51st Symposium on Microelectronics, Pasadena, CA, 2018}}, keywords = {{wire bonding, multi-objective optimization, process model, copper wire, self-optimization}}, title = {{{Intelligent Production of Wire Bonds using Multi-Objective Optimization – Insights, Opportunities and Challenges}}}, doi = {{10.4071/2380-4505-2018.1.000572}}, volume = {{Vol. 2018, No. 1, pp. 000572-000577.}}, year = {{2018}}, } @inproceedings{48839, abstract = {{We analyze the effects of including local search techniques into a multi-objective evolutionary algorithm for solving a bi-objective orienteering problem with a single vehicle while the two conflicting objectives are minimization of travel time and maximization of the number of visited customer locations. Experiments are based on a large set of specifically designed problem instances with different characteristics and it is shown that local search techniques focusing on one of the objectives only improve the performance of the evolutionary algorithm in terms of both objectives. The analysis also shows that local search techniques are capable of sending locally optimal solutions to foremost fronts of the multi-objective optimization process, and that these solutions then become the leading factors of the evolutionary process.}}, author = {{Bossek, Jakob and Grimme, Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{978-1-4503-5618-3}}, keywords = {{combinatorial optimization, metaheuristics, multi-objective optimization, orienteering, transportation}}, pages = {{585–592}}, publisher = {{Association for Computing Machinery}}, title = {{{Local Search Effects in Bi-Objective Orienteering}}}, doi = {{10.1145/3205455.3205548}}, year = {{2018}}, } @inproceedings{48867, abstract = {{Assessing the performance of stochastic optimization algorithms in the field of multi-objective optimization is of utmost importance. Besides the visual comparison of the obtained approximation sets, more sophisticated methods have been proposed in the last decade, e. g., a variety of quantitative performance indicators or statistical tests. In this paper, we present tools implemented in the R package ecr, which assist in performing comprehensive and sound comparison and evaluation of multi-objective evolutionary algorithms following recommendations from the literature.}}, author = {{Bossek, Jakob}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-5764-7}}, keywords = {{evolutionary optimization, performance assessment, software-tools}}, pages = {{1350–1356}}, publisher = {{Association for Computing Machinery}}, title = {{{Performance Assessment of Multi-Objective Evolutionary Algorithms with the R Package ecr}}}, doi = {{10.1145/3205651.3208312}}, year = {{2018}}, } @inproceedings{48885, abstract = {{Performance comparisons of optimization algorithms are heavily influenced by the underlying indicator(s). In this paper we investigate commonly used performance indicators for single-objective stochastic solvers, such as the Penalized Average Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a methodology for analyzing the effects of (usually heuristically set) indicator parametrizations - such as the penalty factor and the method used for aggregating across multiple runs - w.r.t. the robustness of the considered optimization algorithms.}}, author = {{Kerschke, Pascal and Bossek, Jakob and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-5764-7}}, keywords = {{algorithm selection, optimization, performance measures, transportation, travelling salesperson problem}}, pages = {{1737–1744}}, publisher = {{Association for Computing Machinery}}, title = {{{Parameterization of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP Solvers}}}, doi = {{10.1145/3205651.3208233}}, year = {{2018}}, } @phdthesis{9994, abstract = {{Reliability-adaptive systems allow an adaptation of system behavior based on current system reliability. They can extend their lifetime at the cost of lowered performance or vice versa. This can be used to adapt failure behavior according to a maintenance plan, thus increasing availability while using up system capability fully. To facilitate setup, a control algorithm independent of a degradation model is desired. A closed loop control technique for reliability based on a health index, a measure for system degradation, is introduced. It uses self-optimization as means to implement behavior adaptation. This is based on selecting the priorities of objectives that the system pursues. Possible working points are computed beforehand using model-based multiobjective optimization techniques. The controller selects the priorities of objectives and this way balances reliability and performance. As exemplary application, an automatically actuated single plate dry clutch is introduced. The entire reliability control is setup and lifetime experiments are conducted. Results show that the variance of time to failure is reduced greatly, making the failure behavior more predictable. At the same time, the desired usable lifetime can be extended at the cost of system performance to allow for changed maintenance intervals. Together, these possibilities allow for greater system usage and better planning of maintenance.}}, author = {{Meyer, Tobias}}, keywords = {{dependability, reliability, behavior adaptation, self-optimization, multiobjective optimization, optimal control, automotive drivetrain, clutch system, reliability-adaptive system}}, publisher = {{Shaker}}, title = {{{Optimization-based reliability control of mechatronic systems}}}, year = {{2018}}, } @article{9862, abstract = {{In order to improve the credibility of modern simulation tools, uncertainties of different kinds have to be considered. This work is focused on epistemic uncertainties in the framework of continuum mechanics, which are taken into account by fuzzy analysis. The underlying min-max optimization problem of the extension principle is approximated by α-discretization, resulting in a separation of minimum and maximum problems. To become more universal, so-called quantities of interest are employed, which allow a general formulation for the target problem of interest. In this way, the relation to parameter identification problems based on least-squares functions is highlighted. The solutions of the related optimization problems with simple constraints are obtained with a gradient-based scheme, which is derived from a sensitvity analysis for the target problem by means of a variational formulation. Two numerical examples for the fuzzy analysis of material parameters are concerned with a necking problem at large strain elastoplasticity and a perforated strip at large strain hyperelasticity to demonstrate the versatility of the proposed variational formulation. }}, author = {{Mahnken, Rolf}}, issn = {{ 2325-3444}}, journal = {{Mathematics and Mechanics of complex systems}}, keywords = {{fuzzy analysis, α-level optimization, quantities of interest, optimization with simple constraints, large strain elasticity, large strain elastoplasticity}}, number = {{3-4}}, title = {{{"A variational formulation for fuzzy analysis in continuum mechanics"}}}, volume = {{5}}, year = {{2017}}, } @article{9976, abstract = {{State-of-the-art mechatronic systems offer inherent intelligence that enables them to autonomously adapt their behavior to current environmental conditions and to their own system state. This autonomous behavior adaptation is made possible by software in combination with complex sensor and actuator systems and by sophisticated information processing, all of which make these systems increasingly complex. This increasing complexity makes the design process a challenging task and brings new complex possibilities for operation and maintenance. However, with the risk of increased system complexity also comes the chance to adapt system behavior based on current reliability, which in turn increases reliability. The development of such an adaption strategy requires appropriate methods to evaluate reliability based on currently selected system behavior. A common approach to implement such adaptivity is to base system behavior on different working points that are obtained using multiobjective optimization. During operation, selection among these allows a changed operating strategy. To allow for multiobjective optimization, an accurate system model including system reliability is required. This model is repeatedly evaluated by the optimization algorithm. At present, modeling of system reliability and synchronization of the models of behavior and reliability is a laborious manual task and thus very error-prone. Since system behavior is crucial for system reliability, an integrated model is introduced that integrates system behavior and system reliability. The proposed approach is used to formulate reliability-related objective functions for a clutch test rig that are used to compute feasible working points using multiobjective optimization.}}, author = {{Kaul, Thorben and Meyer, Tobias and Sextro, Walter}}, journal = {{SAGE Journals}}, keywords = {{Integrated model, reliability, system behavior, Bayesian network, multiobjective optimization}}, pages = {{390 -- 399}}, title = {{{Formulation of reliability-related objective functions for design of intelligent mechatronic systems}}}, doi = {{10.1177/1748006X17709376}}, volume = {{Vol. 231(4)}}, year = {{2017}}, } @inproceedings{10676, author = {{Ho, Nam and Kaufmann, Paul and Platzner, Marco}}, booktitle = {{2017 International Conference on Field Programmable Technology (ICFPT)}}, keywords = {{Linux, cache storage, microprocessor chips, multiprocessing systems, LEON3-Linux based multicore processor, MiBench suite, block sizes, cache adaptation, evolvable caches, memory-to-cache-index mapping function, processor caches, reconfigurable cache mapping optimization, reconfigurable hardware technology, replacement strategies, standard Linux OS, time a complete hardware implementation, Hardware, Indexes, Linux, Measurement, Multicore processing, Optimization, Training}}, pages = {{215--218}}, title = {{{Evolvable caches: Optimization of reconfigurable cache mappings for a LEON3/Linux-based multi-core processor}}}, doi = {{10.1109/FPT.2017.8280144}}, year = {{2017}}, } @inproceedings{48863, abstract = {{The novel R package ecr (version 2), short for Evolutionary Computation in R, provides a comprehensive collection of building blocks for constructing powerful evolutionary algorithms for single- and multi-objective continuous and combinatorial optimization problems. It allows to solve standard optimization tasks with few lines of code using a black-box approach. Moreover, rapid prototyping of non-standard ideas is possible via an explicit, white-box approach. This paper describes the design principles of the package and gives some introductory examples on how to use the package in practise.}}, author = {{Bossek, Jakob}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference Companion}}, isbn = {{978-1-4503-4939-0}}, keywords = {{evolutionary optimization, software-tools}}, pages = {{1187–1193}}, publisher = {{Association for Computing Machinery}}, title = {{{Ecr 2.0: A Modular Framework for Evolutionary Computation in R}}}, doi = {{10.1145/3067695.3082470}}, year = {{2017}}, } @inproceedings{48857, abstract = {{While finding minimum-cost spanning trees (MST) in undirected graphs is solvable in polynomial time, the multi-criteria minimum spanning tree problem (mcMST) is NP-hard. Interestingly, the mcMST problem has not been in focus of evolutionary computation research for a long period of time, although, its relevance for real world problems is easy to see. The available and most notable approaches by Zhou and Gen as well as by Knowles and Corne concentrate on solution encoding and on fairly dated selection mechanisms. In this work, we revisit the mcMST and focus on the mutation operators as exploratory components of evolutionary algorithms neglected so far. We investigate optimal solution characteristics to discuss current mutation strategies, identify shortcomings of these operators, and propose a sub-tree based operator which offers what we term Pareto-beneficial behavior: ensuring convergence and diversity at the same time. The operator is empirically evaluated inside modern standard evolutionary meta-heuristics for multi-criteria optimization and compared to hitherto applied mutation operators in the context of mcMST.}}, author = {{Bossek, Jakob and Grimme, Christian}}, booktitle = {{2017 IEEE Symposium Series on Computational Intelligence (SSCI)}}, keywords = {{Convergence, Encoding, Euclidean distance, Evolutionary computation, Heating systems, Optimization, Standards}}, pages = {{1–8}}, title = {{{A Pareto-Beneficial Sub-Tree Mutation for the Multi-Criteria Minimum Spanning Tree Problem}}}, doi = {{10.1109/SSCI.2017.8285183}}, year = {{2017}}, } @inproceedings{9966, abstract = {{Usage of copper wire bonds allows to push power boundaries imposed by aluminum wire bonds. Copper allows higher electrical, thermal and mechanical loads than aluminum, which currently is the most commonly used material in heavy wire bonding. This is the main driving factor for increased usage of copper in high power applications such as wind turbines, locomotives or electric vehicles. At the same time, usage of copper also increases tool wear and reduces the range of parameter values for a stable process, making the process more challenging. To overcome these drawbacks, parameter adaptation at runtime using self-optimization is desired. A self-optimizing system is based on system objectives that evaluate and quantify system performance. System parameters can be changed at runtime such that pre-selected objective values are reached. For adaptation of bond process parameters, model-based self-optimization is employed. Since it is based on a model of the system, the bond process was modeled. In addition to static model parameters such as wire and substrate material properties and vibration characteristics of transducer and tool, variable model inputs are process parameters. Main simulation result is bonded area in the wiresubstrate contact. This model is then used to find valid and optimal working points before operation. The working point is composed of normal force and ultrasonic voltage trajectories, which are usually determined experimentally. Instead, multiobjective optimalization is used to compute trajectories that simultaneously optimize bond quality, process duration, tool wear and probability of tool-substrate contacts. The values of these objectives are computed using the process model. At runtime, selection among pre-determined optimal working points is sufficient to prioritize individual objectives. This way, the computationally expensive process of numerically solving a multiobjective optimal control problem and the demanding high speed bonding process are separated. To evaluate to what extent the pre-defined goals of self-optimization are met, an offthe- shelf heavy wire bonding machine was modified to allow for parameter adaptation and for transmitting of measurement data at runtime. This data is received by an external computer system and evaluated to select a new working point. Then, new process parameters are sent to the modified bonding machine for use for subsequent bonds. With these components, a full self-optimizing system has been implemented.}}, author = {{Meyer , Tobias and Unger, Andreas and Althoff, Simon and Sextro, Walter and Brökelmann, Michael and Hunstig, Matthias and Guth, Karsten}}, booktitle = {{IEEE 66th Electronic Components and Technology Conference}}, keywords = {{Self-optimization, adaptive system, bond process, copper wire}}, pages = {{622--628}}, title = {{{Reliable Manufacturing of Heavy Copper Wire Bonds Using Online Parameter Adaptation}}}, doi = {{10.1109/ECTC.2016.215}}, 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}}, } @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{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{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{9884, abstract = {{So-called reliability adaptive systems are able to adapt their system behavior based on the current reliability of the system. This allows them to react to changed operating conditions or faults within the system that change the degradation behavior. To implement such reliability adaptation, self-optimization can be used. A self-optimizing system pursues objectives, of which the priorities can be changed at runtime, in turn changing the system behavior. When including system reliability as an objective of the system, it becomes possible to change the system based on the current reliability as well. This capability can be used to control the reliability of the system throughout its operation period in order to achieve a pre-defined or user-selectable system lifetime. This way, optimal planning of maintenance intervals is possible while also using the system capabilities to their full extent. Our proposed control system makes it possible to react to changed degradation behavior by selecting objectives of the self-optimizing system and in turn changing the operating parameters in a closed loop. A two-stage controller is designed which is used to select the currently required priorities of the objectives in order to fulfill the desired usable lifetime. Investigations using a model of an automotive clutch system serve to demonstrate the feasibility of our controller. It is shown that the desired lifetime can be achieved reliably.}}, author = {{Meyer , Tobias and Sextro, Walter}}, booktitle = {{Proceedings of the Second European Conference of the Prognostics and Health Management Society 2014}}, keywords = {{self-optimization reliability adaptive}}, title = {{{Closed-loop Control System for the Reliability of Intelligent Mechatronic Systems}}}, volume = {{5}}, year = {{2014}}, } @article{9885, abstract = {{Intelligent mechatronic systems, such as self-optimizing systems, allow an adaptation of the system behavior at runtime based on the current situation. To do so, they generally select among several pre-defined working points. A common method to determine working points for a mechatronic system is to use model-based multiobjective optimization. It allows finding compromises among conflicting objectives, called objective functions, by adapting parameters. To evaluate the system behavior for different parameter sets, a model of the system behavior is included in the objective functions and is evaluated during each function call. Intelligent mechatronic systems also have the ability to adapt their behavior based on their current reliability, thus increasing their availability, or on changed safety requirements; all of which are summed up by the common term dependability. To allow this adaptation, dependability can be considered in multiobjective optimization by including dependability-related objective functions. However, whereas performance-related objective functions are easily found, formulation of dependability-related objective functions is highly system-specific and not intuitive, making it complex and error-prone. Since each mechatronic system is different, individual failure modes have to be taken into account, which need to be found using common methods such as Failure-Modes and Effects Analysis or Fault Tree Analysis. Using component degradation models, which again are specific to the system at hand, the main loading factors can be determined. By including these in the model of the system behavior, the relation between working point and dependability can be formulated as an objective function. In our work, this approach is presented in more detail. It is exemplified using an actively actuated single plate dry clutch system. Results show that this approach is suitable for formulating dependability-related objective functions and that these can be used to extend system lifetime by adapting system behavior.}}, author = {{Meyer , Tobias and Sondermann-Wölke, Christoph and Sextro, Walter}}, journal = {{Conference Proceedings of the 2nd International Conference on System-Integrated Intelligence}}, keywords = {{Self-optimization, multiobjective optimization, objective function, dependability, intelligent system, behavior adaptation}}, pages = {{46--53}}, title = {{{Method to Identify Dependability Objectives in Multiobjective Optimization Problem}}}, doi = {{10.1016/j.protcy.2014.09.033}}, volume = {{15}}, year = {{2014}}, } @inproceedings{46397, 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 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.}}, author = {{Brockhoff, Dimo and Wagner, Tobias and Trautmann, Heike}}, booktitle = {{Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation}}, isbn = {{9781450311779}}, keywords = {{hypervolume indicator, multiobjective optimization, performance assessment, r2 indicator}}, pages = {{465–472}}, publisher = {{Association for Computing Machinery}}, title = {{{On the Properties of the R2 Indicator}}}, doi = {{10.1145/2330163.2330230}}, year = {{2012}}, }