Minisymposia

Minisymposia

Accepted minisymposia are listed below.

Potential attendees can find the organizers contact in the description for further information.

The template for the extended abstract and full paper are available at http://eurogen2017.etsiae.upm.es/eurogen2017/submissions/.
Please, keep in mind that both extended abstract and final full paper must be submitted following these template format.

Strategic interaction: theoretical and computational questions of Optimization and Game Theory

Organizers

Carlo de Nicola, Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, University of Naples Federico II, denicola@unina.it
Lina Mallozzi, Dipartimento di Matematica e Applicazioni University of Naples Federico II, mallozzi@unina.it

Abstract

In 1987 the first published research appeared which used the Genetic Algorithm as a means of seeking better strategies in playing the repeated Prisoners Dilemma [Axelrod R., The Evolution of Strategies in the Iterated Prisoners Dilemma]. Since then the application of Genetic Algorithms to game theoretical models has been used in many contexts. This minisymposium intends to focus on some problems arising in Optimization and in Game Theory, that can be approached by means of genetic algorithms procedures and that correspond to applicative models from Engineering, Economics, Aeronautics, Computer Science, Physics, among the others.

Topics

Authors are invited to submit papers on one or more of the following topics:

  • non-cooperative games
  • cooperative solutions
  • networks
  • robust optimization
  • multilevel optimization
  • efficiency and Pareto optimality
  • locational problems
  • dynamics and stability
Surrogate-assisted Optimization of Real World problems

Organizers

Daniel González, Fluid Dynamics Branch, Instituto Nacional de Técnica Aeroespacial – INTA, gonzalezjd@inta.es
Emiliano Iuliano, Fluid Mechanics Department, Centro Italiano Ricerche Aerospaziali - CIRA, e.iuliano@cira.it

Abstract

During the last years evolutionary algorithms (EAs) have become an everyday tool in real life design/optimization problems. EAs can handle complex, constrained, multi-objective optimization problems with noisy objective functions using any analysis software as "black-box". Moreover, they do not require strict conditions on the objective function behavior (e.g., continuity) and have shown very effective in exploring the design space in a global sense in order to locate the global optimum candidates. However, they require a vast number of evaluations to reach the optimal solutions even in problems with a few design variables. Since each objective function evaluation involves the computational solution by means of expensive high-fidelity tools like CFD, CSM, FEA, EAs become computationally prohibitive for real-world applications.

Therefore, there has been a raising interest in techniques that may reduce the computational cost of an EA-based optimization. Among the others, the most popular is the use of surrogate models (or metamodels) to reduce the number of calls to the high-fidelity simulation tool.

Metamodels are low cost approximations to the costly analysis model (CFD or CSM software etc.) able to provide a fast and sufficiently accurate estimation of the quantities of interest of the simulation (e.g., the aerodynamic coefficients) and, hence, of the objective function used in the optimization process.
In 2013, a GARTEUR Action Group was established to explore surrogate-based global optimization approaches. The main objective of the AG work is, by means of a European collaborative research, to make a deep evaluation and assessment of surrogate-based global optimization methods for aerodynamic shape optimization, dealing with the main challenges as the curse of dimensionality, reduction of the design space and error metrics for model validation, amongst others.

This minisymposium aims at collecting and disseminating new ideas in surrogate modeling and surrogate-based optimization as well as their application to real world problems. Emphasis is laid on the development and use of fast and efficient metamodels for real-world industrial optimization problems where multimodality, high non-linearity, non-differentiability, high dimensionality and high computational cost are expected.

Metaheuristics and Evolutionary Algorithm in Energy

Organizers

Diego Oliva, Tecnológico de Monterrey, Mexico, doliva@ucm.es

Abstract

The aim of this mini-symposia is to congregate researchers in the field of Energy. Several problems in this area are treated as optimization problems and solved using metaheuristics or evolutionary algorithms. Such problems are not trivial, and they represent real life situations that also affects the future of some countries. In this sense, it is necessary to know the recent advances and applications of different optimization algorithms to find the best solutions for the problems that Energy represents. This mini-symposia is open to any kind of Energy (solar, wind, fossil, etc).

Extension of fixed point PDE solvers for optimal design - Methods and Applications

Organizers

Nicolas R. Gauger, TU Kaiserslautern, nicolas.gauger@scicomp.uni-kl.de
Lisa Kusch, TU Kaiserslautern, lisa.kusch@scicomp.uni-kl.de

Abstract

Adjoint methods are efficient and successful tools for optimal design under PDE constraints, as sensitivities can be obtained by one evaluation of the adjoint equation. Consistency and robustness of adjoint-based optimization strategies can be obtained in all areas of application where PDEs are treated by using a contractive fixed point solver. When differentiating the entire fixed point iterator, the discrete adjoint solver directly inherits the convergence properties of the primal flow solver, and the adjoint solution is consistent in a discrete setting with the primal solution. Advanced techniques in the development of algorithmic differentiation facilitate the extension of the fixed point solver when it is modified, as well as the incorporation of different objective functions and constraints for optimization. Further optimization for increasing the efficiency of the discrete adjoint solver can be made. The use of consistent discrete adjoint solvers extending the primal fixed point solver enables a direct transition from simulation to optimization. In one-shot methods, for example, the derivative information is directly employed to simultaneously achieve optimality together with primal and adjoint feasibility.

Any contributions involving methods and applications in the field of steady and unsteady PDE-constrained optimal design are welcomed. Some examples of topics are:

  • development of discrete adjoint solvers
  • applications in aerodynamic, acoustics, structural mechanics
  • multi-physics applications
  • development and application of one-shot methods
Optimum design applications in structural and civil engineering

Organizers

David Greiner, Universidad de Las Pala de Gran Canaria (ULPGC), david.greiner@ulpgc.es
Jorge Magalhaes-Mendes, Politécnico do Porto, jjm@isep.ipp.pt
Jacques Periaux, International Center for Numerical Methods in Engineering (CIMNE), jperiaux@gmail.com

Abstract

Optimum design in Structural and Civil Engineering has become a major research topic in recent decades. It has benefit from the recent advances in optimization techniques, both metaheuristic and bioinspired ones, as well as the deterministic approaches and classical techniques.

The main objective of this mini-symposium is to bring together researchers and to generate interest in presenting papers on new approaches, in the field of optimization in structural and civil engineering.

Communications must address optimization techniques, applied in solving optimum design problems in structural and civil engineering and related topics [1, 2].
Evolutionary algorithms and Metaheuristics are an interdisciplinary research area comprising several paradigms inspired by the Darwinian principle of evolution and Bioinspired Algorithms. The current stage of research considers, among others, the following paradigms: Genetic Algorithms, Genetic.

Programming, Evolution Strategies, Differential Evolution, etc. in addition to other metaheuristic paradigms such as Particle Swarm Optimization or Ant Colony Optimization. Classical optimization and deterministic approaches techniques, and their development and applications, e.g. in topology optimization, are also welcome. Applications of these optimization methods and others (e.g. see [3, 4]), in structural and civil engineering are welcomed, both for single-objective and multi-objective optimization problems [5].

Topics to be covered (but not limited to) are: Structural design (e.g.: concrete and/or steel structures, etc.)[6], geotechnics, acoustics, hydraulics, and infrastructure. Also in the field of construction engineering: project management, planning, coordination and control of projects, cost and time management, among others. Development aspects such as surrogate modeling, parallelization, game strategies based hybridization [7, 8], performance comparisons among methods, etc., are encouraged.

Some application topics examples are:

  • Acoustics
  • Earthquake Engineering
  • Fracture Mechanics
  • Linear and Non-linear Dynamics
  • Geotechnical Analysis and Design
  • Multi Scale Modelling, Multi-Scale Analysis
  • Reliability-based Design Optimization (RBDO)
  • Slope Design
  • Space, Tension and Shell Structures
  • Structural Control
  • Structural Damage Detection and Identification
  • Structural Engineering including Steel, Composite and Reinforced Concrete Structures

Key words: Evolutionary Algorithms, Genetic Algorithms, Metaheuristics, Computational Methods, Civil Engineering, Construction Management, Optimum Design, Robust Design.

REFERENCES

[1] N. Lagaros, M Papadrakakis “Engineering and Applied Sciences Optimization”. Computational Methods in Applied Sciences”, Computational Methods in Applied Sciences, Springer, Vol. 38, 2015

[2] J. Magalhães-Mendes, D. Greiner, "Evolutionary Algorithms and Metaheuristics in Civil Engineering and Construction Management", Computational Methods in Applied Sciences, Springer, Vol. 39, 2015

[3] P. Neittaanmäki, S. Repin, T. Tuovinen, “Mathematical Modeling and Optimization of Complex Structures”, Computational Methods in Applied Sciences, vol. 40, Springer, 2016

[4] D. Greiner, B. Galván, J. Periaux, N. Gauger, K. Giannakoglou, G. Winter, “Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences”, Computational methods in applied sciences, vol. 36. Springer, 2015

[5] C. Coello Coello, “Evolutionary Multi-Objective Optimization: A Historical View of the Field”, IEEE Computational Intelligence Magazine, 1, 28-36, 2006

[6] R. Kicinger, T. Arciszewski, KA. De Jong, “Evolutionary Computation and structural design: A survey of the state of the art“, Computers & Structures, 83, 1943-1978, 2005

[7] J. Periaux, F. González, DS. Lee, “Evolutionary optimization and game strategies for advanced multi-disciplinary design”, In: Intelligent Systems, Control and Automation: Science and Engineering Series, vol. 75, Springer, 2015

[8] D. Greiner, J. Periaux, JM. Emperador, B. Galván, G. Winter, “Game Theory Based Evolutionary Algorithms: A Review with Nash Applications in Structural Engineering Optimization Problems”, Archives of Computational Methods in Engineering, pp 1–48, 2016

Fall detection and prevention in the elderly

Organizers

Alberto Brunete Gonzalez, Universidad Politécnica de Madrid, alberto.brunete@upm.es
Miguel Hernando, Universidad Politécnica de Madrid, miguel.hernando@upm.es
Ernesto Gambao, Universidad Politécnica de Madrid, ernesto.gambao@upm.es

Abstract

The risk of falling is one of the most prevalent problems faced by elderly individuals. A study published by the World Health Organization estimates that between 28% and 35% of people over 65 years old suffer at least one fall each year. Prompt detection of the fall is essential to minimize the damages. But prevention of the fall is even more important. Papers presenting algorithms and decision making mechanisms for detection and prevention of falls are welcome. Topics include but are not limited to: multi-criteria decision making, evolutionary algorithms, optimization methods, sensor fusion, etc.

Multi-disciplinary Design Optimisation

Organizers

Annalisa Riccardi, University of Strathclyde, annalisa.riccardi@strath.ac.uk
Edmondo Minisci, University of Strathclyde, edmondo.minisci@strath.ac.uk
Massimiliano Vasile, University of Strathclyde, massimiliano.vasile@strath.ac.uk

Abstract

Across all fields of Engineering Sciences, many design problems are multidisciplinary in nature. An optimal design can be achieved if all the disciplines are concurrently considered in an integrated approach. In MDO the whole is more than the sum of the parts, therefore the optimum of the integrated problem is superior to the design found by optimizing each discipline independently. However, including all disciplines simultaneously significantly increases the complexity of the problem. The optimal design of each discipline can be in itself a hard and computationally intensive optimization problem. In addition, the definition of the level of fidelity of the model for each discipline, the interexchange of variables of different nature (the output of one discipline can become the input to another) and the increased dimensionality, contribute to make the problem considerably harder. The largest number of applications is in the field of aerospace engineering, such as aircraft and spacecraft design in which aerodynamics, structural analysis, propulsion, control theory, and economics are integrated in a single optimization process. But many techniques have been developed and applied in a number of different fields, including automotive design, naval architecture, electronics, computers, and electricity distribution.

This special session intends to collect many, diverse efforts made in the development of methods and techniques for multidisciplinary design optimization across all the fields of engineering and physical sciences. The session seeks to bring together researchers from around the globe for a stimulating discussion on recent advances in MDO methods for the solution of any engineering problem. The session looks with particular interest for (but not limited to) nature inspired methods specifically devised, adapted or tailored to address problems in MDO applications or nature inspired methods that were demonstrated to be particularly effective at solving MDO related problems. Furthermore, new examples of real-world applications of MDO techniques are welcome. Authors are invited to submit papers on one or more of the following topics:

  • Multi-Objective Optimization Methods in MDO
  • Evolutionary Optimisation in MDO
  • Uncertainty Treatment in MDO
  • Integrated System and Control Design
  • Optimization by Multi-fidelity Modelling
  • Optimization by Space Reduction Techniques
  • Concurrent Engineering and Distributed CE
  • Distributed and Parallel MDO
  • Game Theory Approaches to MDO
  • Topology MDO
  • Data analytic for MDO
  • Knowledge Based Engineering for MDO
  • Model Based Systems Engineering for MDO
Applications of optimization in engineering design automation

Organizers

Doris Entner, V-Research GmbH,doris.entner@v-research.at
Thorsten Prante, V-Research GmbH, thorsten.prante@v-research.at
Michael Affenzeller, University of Applied Science Upper Austria, michael.affenzeller@fh-hagenberg.at
Joel Johansson, Jönköping University, joel.johansson@ju.se
Wim J.C. Verhagen, Delft University of Technology, w.j.c.verhagen@tudelft.nl

Abstract

Based on a history of automating design calculations, simulation support, and applying AI and optimization in engineering domains, design automation (DA) is nowadays most commonly employed to streamline embodiment and detail stage design tasks with pre-structured solution spaces, in order to reduce cost, lead times and error rates (i.e. information waste [1]) [2]. While, e.g. reduced lead times often result in faster time-to-market, they at the same time also contribute to minimized uncertainties and risks since market needs will change less in shorter time spans. More recently, DA is also applied to facilitate solution finding related to conceptual and embodiment stage tasks with large and unstructured solution spaces [3]. This latter type of DA is thus used to achieve anything between improving solution quality (e.g. based on optimization) and rendering for human intractable tasks tractable. Both introduced types of DA reduce complexities in design phases and are supportive to the striving to explore the design envelope to the fullest extent possible before making decisions with a high impact on committed costs [4].

Optimization problems are often an inherent part of DA solutions since the complexity of products often yields optimization tasks which exceed the human capacity to solve them manually [5]–[7]. The complexity can be of various forms [8], e.g. of structural, behavioral, technological nature or due to very specific customer-requirements. The usage of computational optimization methods within DA is thus of utmost importance in order to obtain (near) optimal solutions. A wide range of industries has adopted optimization problems within engineering DA e.g. for validating and improving structural aspects (e.g. using FEA and simulation-based optimization [9], [10]), or appropriately selecting, dimensioning and assembling components to fulfil customer-specific needs (e.g. combinatorial problems such as the facility layout problem [11] or other configuration-based problems [12], [13]). Beyond, also optimization methods to guide solution finding related to tasks with large and unstructured solution spaces have received increased attention [14].

Through the increase in computational power and the wider availability of IT-infrastructure enabling distribution and parallelization of computational tasks [15], also more advanced optimization methods (e.g. meta-heuristics including evolutionary algorithms) are no longer out of reach for practitioners. Furthermore, many optimization methods do not only generate one, but several (near) optimal solutions, which can give engineers a new impulse to think out of the box in order to design novel solutions. Finally, conflicting interests can be included in multi-objective optimization procedures, which generate solutions at the Pareto-front, and allow a trade-off of the different optimization criteria.

This symposium aims at bringing together practitioners and scientists from academia, research institutes and industry to discuss about practical and scientific problems and solution strategies in the context of engineering design automation and optimization. For the symposium participants, this will give the opportunity to exchange experience about and gain insight from both industry and academic view-points as to this topic. The aim is to contribute to community building with a focus on optimization in the context of engineering design automation.

Expected Contributions / Topics of Interest

We welcome all contributions with an industrial application or directed towards being applicable in an industrial contexts of optimization in engineering design automation. Possible approaches include

  • Single- and multi-objective optimization methods (linear / nonlinear, trajectory-based / evolutionary algorithms),
  • Simulation-based optimization including surrogate modelling,
  • Multi-disciplinary optimization,
  • Visualization of solution spaces and trade-off of solutions
  • Optimization and interactivity
  • Optimization under uncertainty
  • Sensitivity analysis of optimization solutions
  • Integration of optimization in DA, and
  • Software platforms and IT infrastructures for supporting optimization in DA.

Areas of application include, but are not limited to,

  • Mechanical and plant engineering,
  • Aerospace
  • Automotive
  • Energy Systems

References

[1] W. J. C. Verhagen, B. de Vrught, J. Schut, and R. Curran, “A method for identification of automation potential through modelling of engineering processes and quantification of information waste,” Advanced Engineering Informatics, 2015.

[2] J. Johansson and F. Elgh, “How to Successfully Implement Automated Engineering Design Systems: Reviewing Four Case Studies.,” in 20th ISPE International Conference on Concurrent Engineering, 2013, pp. 173–182.

[3] A. Chakrabarti et al., “Computer-Based Design Synthesis Research: An Overview,” Journal of Computing and Information Science in Engineering, vol. 11, 2011.

[4] W. J. C. Verhagen, P. Bermell-Garcia, R. E. C. van Dijk, and R. Curran, “A critical review of Knowledge-Based Engineering: An identification of research challenges,” Advanced Engineering Informatics, vol. 26, no. 1, pp. 5–15, Jan. 2012.

[5] R. Roy, S. Hinduja, and R. Teti, “Recent advances in engineering design optimisation: Challenges and future trends,” CIRP Annals-Manufacturing Technology, vol. 57, no. 2, pp. 697–715, 2008.

[6] P. Fleck, M. Kommenda, M. Affenzeller, and T. Prante, “Analysis of Uncertainty in Engineering Design Optimization Problems,” in Proceedings of the 28th European Modeling and Simulation Symposium EMSS 2016, Larnaca, Zypern, 2016, pp. 207–116.

[7] K. Deb, Optimization for Engineering Design: Algorithms and Examples, 11th edition. New Delhi: PHI Learning Pvt. Ltd., 2010.

[8] W. ElMaraghy, H. ElMaraghy, T. Tomiyama, and L. Monostori, “Complexity in engineering design and manufacturing,” CIRP Annals - Manufacturing Technology, vol. 61, no. 2, pp. 793–814, 2012.

[9] M. Affenzeller et al., “Simulation-Based Optimization with HeuristicLab: Practical Guidelines and Real-World Applications,” in Applied Simulation and Optimization, M. M. Mota, I. F. D. L. Mota, and D. G. Serrano, Eds. Springer International Publishing, 2015, pp. 3–38.

[10] A.-C. Zavoianu, E. Lughofer, G. Bramerdorfer, W. Amrhein, and S. Saminger-Platz, “A Surrogate-Based Strategy for Multi-objective Tolerance Analysis in Electrical Machine Design,” in Proceedings of the 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Washington, DC, USA, 2015, pp. 195–203.

[11] A. Kusiak and S. S. Heragu, “The Facility Layout Problem,” European Journal of Operational Research, vol. 29, no. 3, pp. 229–251, 1987.

[12] G. Frank, D. Entner, T. Prante, V. Khachatouri, and M. Schwarz, “Towards a Generic Framework of Engineering Design Automation for Creating Complex CAD Models,” International Journal On Advances in Systems and Measurements, vol. 7, no. 1 and 2, pp. 179–192, Jun. 2014.

[13] L. L. Zhang, “Product configuration: a review of the state-of-the-art and future research,” International Journal of Production Research, vol. 52, no. 21, pp. 6381–6398, Nov. 2014.

[14] C. Münzer and K. Shea, “An Integrated Approach to Automated Synthesis, Simulation and Optimization of Energy and Signal-Based Design Concepts,” in ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 2016, pp. V007T06A035–V007T06A035.

[15] C. Neumüller, A. Scheibenpflug, S. Wagner, A. Beham, and M. Affenzeller, “Large Scale Parameter Meta-Optimization of Metaheuristic Optimization Algorithms with HeuristicLab Hive,” presented at the Actas del VIII Congreso Español sobre Metaheurística, Algorítmos Evolutivos y Bioinspirados (MAEB’2012), Albacete, Spain, 2012, p. 8.

Optimization under Uncertainty

Organizers

Domenico Quagliarella, Centro Italiano Ricerche Aerospaziali (CIRA), d.quagliarella@cira.it
Massimiliano Vasile, University of Strathclyde, massimiliano.vasile@strath.ac.uk

Abstract

Real world design optimization problems often require that the solution meets stringent requirements of robustness and reliability, and the continued progress and advancement of computational capabilities of modern computer systems make it increasingly attractive the idea of introducing uncertainty quantification techniques directly into the optimization loop. On the other hand, the analysis and quantification of the uncertainty has made enormous progresses in recent years since the seminal work of Taguchi on the application of statistical methods to improve the quality of manufactured goods. This special session is conceived as forum of discussion between the research community and industry on the perspectives of the rapidly growing field of optimization under uncertainty. The present session will focus on both industrial applications and basic research to bring together practitioners in a field that is experiencing enormous broadening. Authors are encouraged to propose either success stories of application of optimization under uncertainty in industrial contexts or to present the latest developments of basic research in this exciting field.

Key words: Uncertainty quantification, Optimization under uncertainty, Robust design, Reliability based design methods.

Topics

Authors are invited to submit papers on any topic related to optimization under uncertainty. More specifically, they may focus one or more of the following topics:

  • Industrial applications of optimization in presence of uncertainties
  • Large scale problems
  • Worst case scenario
  • Robust design
  • Reliability-based design
  • Multi and many object optimization under uncertainty
  • Evidence-based approaches and decision making
  • Evolvable optimization under uncertainty
  • Innovative/alternative approaches to optimization under uncertainty
  • Methods to speed-up computationally expensive problems
Adjoint Methods for Optimisation, Mesh Adaptation and Uncertainty Quantification

Organizers

Jens-Dominik Mueller, Queen Mary University of London, j.mueller@qmul.ac.uk
Kyriakos Giannakoglou, National Technical University of Athens, kgianna@central.ntua.gr
Tom Verstraete, Queen Mary University of London, t.verstraete@qmul.ac.uk

Abstract

The adjoint method is recognised as the most efficient method to compute gradients for numerical optimisation, mesh adaptation, or uncertainty quantification. The major European industries and research institutions and commercial vendors have developed adjoint CFD solvers, but there is also strong interest in CSM, CAA or CHT. The EC has funded a number of projects on this in particular currently the MC-ITN project IODA:
Industrial Optimal Design using Adjoint CFD: http://ioda.sems.qmul.ac.uk/

Topics

This minisymposium focuses on the topic of adjoint-based optimisation for steady and unsteady flows. Contributions are welcome on:

  • Improving robustness and versatility of the adjoint solvers
  • Progress toward with adjoints for unsteady flows
  • Integration into the workflow with parametrisation, optimisation and return to CAD
  • Adjoint methods in uncertainty quantification
  • Error analysis and adjoint-driven mesh adaptation
  • Applications of adjoint design in industrial cases
Sensitivity and adjoint methods for optimization in flow stability problems

Organizers

Eusebio Valero, Universidad Politécnica de Madrid, eusebio.valero@upm.es
Alejandro Martínez-Cava , Universidad Politécnica de Madrid, alejandro.martinezcava@upm.es
Andrés Rueda, Universidad Politécnica de Madrid, amrueda@gmail.com

Abstract

Flow stability techniques predict how small flow perturbations grow or decay with respect to a stable flow solution, providing valuable information about how the transition to unsteadiness appears and the means that help to control it. Stability analysis can be applied to study configurations in the limits of the flight envelope or when unsteady effects are dominant.
The combination of stability analysis, adjoint and sensitivity methods is able to compute the gradients of a particular flow feature to perturbations introduced by, e.g. surface deformations, noise or external forces and perform optimization loops to obtain more efficient designs where the target is a particular feature of the flow.

More specific topics are:

  • Development of new numerical methods and tools better suited for stability analysis and direct numerical simulation, capable of capturing the growth of small perturbations in the flow.
  • Combination of the main theoretical/numerical capabilities to improve understanding of unsteady flows, flow control devices and flow instability.
  • Identification of quantified aerodynamic benefits in terms of drag reduction, lifting surface size reduction (weight and fuel consumption reduction), aircraft operational maneuvers (stall conditions), noise emissions (Green Air Transport Operations) and aircraft operational safety (tail buffet alleviation).
  • Optimization loops to investigate the control/ suppression of unsteady flow through flow control devices or surface modifications, by identifying the most suitable zones which have most influence on the flow features.