Jürgen Teich

Bio: Jürgen Teich is an academic researcher from University of Erlangen-Nuremberg. The author has contributed to research in topics: Design space exploration & Control reconfiguration. The author has an hindex of 49, co-authored 654 publications receiving 11428 citations. Previous affiliations of Jürgen Teich include University of Paderborn & Fraunhofer Society.

Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO)

Abstract: In multi-objective particle swarm optimization (MOPSO) methods, selecting the best local guide (the global best particle) for each particle of the population from a set of Pareto-optimal solutions has a great impact on the convergence and diversity of solutions, especially when optimizing problems with high number of objectives. This paper introduces the Sigma method as a new method for finding best local guides for each particle of the population. The Sigma method is implemented and is compared with another method, which uses the strategy of an existing MOPSO method for finding the local guides. These methods are examined for different test functions and the results are compared with the results of a multi-objective evolutionary algorithm (MOEA).

Hardware/Software Codesign: The Past, the Present, and Predicting the Future

Abstract: Hardware/software codesign investigates the concurrent design of hardware and software components of complex electronic systems. It tries to exploit the synergy of hardware and software with the goal to optimize and/or satisfy design constraints such as cost, performance, and power of the final product. At the same time, it targets to reduce the time-to-market frame considerably. This paper presents major achievements of two decades of research on methods and tools for hardware/software codesign by starting with a historical survey of its roots, by highlighting its major research directions and achievements until today, and finally, by predicting in which direction research in codesign might evolve in the decades to come.

Pareto-Front Exploration with Uncertain Objectives

Abstract: We consider the problem of exploration of the set of all global optima (Pareto-points) or an approximation thereof in the context of multi-objective function optimization. Up to now, set oriented techniques assume that the evaluation of the m-dimensional vector of objectives can be done exactly which is important to steer the search process towards global optima. Here, we extend such techniques to allow objectives to be uncertain, i.e., vary within intervals. This may be often the case if the exact computation of objectives is computationally too expensive such that only estimates on the objective values of a design point may be derived. For objective values that are constrained by intervals, we derive a theory of probabilistic dominance, an extension of the definition of Pareto-dominance. Also, we show how this theory may be used in order to guide the selection process to approximate the Pareto-set.

System-Level Synthesis Using Evolutionary Algorithms

Abstract: In this paper, we consider system-level synthesis as the problem of optimally mapping a task-level specification onto a heterogeneous hardware/software architecture. This problem requires (1) the selection of the architecture (allocation) including general purpose and dedicated processors, ASICs, busses and memories, (2) the mapping of the specification onto the selected architecture in space (binding) and time (scheduling), and (3) the design space exploration with the goal to find a set of implementations that satisfy a number of constraints on cost and performance. Existing methodologies often consider a fixed architecture, perform the binding only, do not reflect the tight interdependency between binding and scheduling, do not consider communication (tasks and resources), or require long run-times preventing design space exploration, or yield only one implementation with optimal cost. Here, a model is introduced that handles all mentioned requirements and allows the task of system-synthesis to be specified as an optimization problem. The application and adaptation of an Evolutionary Algorithm to solve the tasks of optimization and design space exploration is described.

Opt4J: a modular framework for meta-heuristic optimization

Abstract: This paper presents a modular framework for meta-heuristic optimization of complex optimization tasks by decomposing them into subtasks that may be designed and developed separately. Since these subtasks are generally correlated, a separate optimization is prohibited and the framework has to be capable of optimizing the subtasks concurrently. For this purpose, a distinction of genetic representation (genotype) and representation of a solution of the optimization problem (phenotype) is imposed. A compositional genotype and appropriate operators enable the separate development and testing of the optimization of subtasks by a strict decoupling. The proposed concept is implemented as open source reference OPT4J [6]. The architecture of this implementation is outlined and design decisions are discussed that enable a maximal decoupling and flexibility. A case study of a complex real-world optimization problem from the automotive domain is introduced. This case study requires the concurrent optimization of several heterogeneous aspects. Exemplary, it is shown how the proposed framework allows to efficiently optimize this complex problem by decomposing it into subtasks that are optimized concurrently.

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach

Abstract: Evolutionary algorithms (EAs) are often well-suited for optimization problems involving several, often conflicting objectives. Since 1985, various evolutionary approaches to multiobjective optimization have been developed that are capable of searching for multiple solutions concurrently in a single run. However, the few comparative studies of different methods presented up to now remain mostly qualitative and are often restricted to a few approaches. In this paper, four multiobjective EAs are compared quantitatively where an extended 0/1 knapsack problem is taken as a basis. Furthermore, we introduce a new evolutionary approach to multicriteria optimization, the strength Pareto EA (SPEA), that combines several features of previous multiobjective EAs in a unique manner. It is characterized by (a) storing nondominated solutions externally in a second, continuously updated population, (b) evaluating an individual's fitness dependent on the number of external nondominated points that dominate it, (c) preserving population diversity using the Pareto dominance relationship, and (d) incorporating a clustering procedure in order to reduce the nondominated set without destroying its characteristics. The proof-of-principle results obtained on two artificial problems as well as a larger problem, the synthesis of a digital hardware-software multiprocessor system, suggest that SPEA can be very effective in sampling from along the entire Pareto-optimal front and distributing the generated solutions over the tradeoff surface. Moreover, SPEA clearly outperforms the other four multiobjective EAs on the 0/1 knapsack problem.

Evolutionary algorithms for solving multi-objective problems

Abstract: List of Figures. List of Tables. Preface. Foreword. 1. Basic Concepts. 2. Evolutionary Algorithm MOP Approaches. 3. MOEA Test Suites. 4. MOEA Testing and Analysis. 5. MOEA Theory and Issues. 3. MOEA Theoretical Issues. 6. Applications. 7. MOEA Parallelization. 8. Multi-Criteria Decision Making. 9. Special Topics. 10. Epilog. Appendix A: MOEA Classification and Technique Analysis. Appendix B: MOPs in the Literature. Appendix C: Ptrue & PFtrue for Selected Numeric MOPs. Appendix D: Ptrue & PFtrue for Side-Constrained MOPs. Appendix E: MOEA Software Availability. Appendix F: MOEA-Related Information. Index. References.

Handling multiple objectives with particle swarm optimization

Abstract: This paper presents an approach in which Pareto dominance is incorporated into particle swarm optimization (PSO) in order to allow this heuristic to handle problems with several objective functions. Unlike other current proposals to extend PSO to solve multiobjective optimization problems, our algorithm uses a secondary (i.e., external) repository of particles that is later used by other particles to guide their own flight. We also incorporate a special mutation operator that enriches the exploratory capabilities of our algorithm. The proposed approach is validated using several test functions and metrics taken from the standard literature on evolutionary multiobjective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multiobjective optimization problems.