Advances in Evolutionary and Deterministic Methods for Design, Optimization and Control in Engineering and Sciences, Edmondo Minisci; Massimiliano Vasile; Jacques Peri
Preface.- Part 1: Theoretical and Numerical Methods and Tools for Optimization.- 1.1 Theoretical Methods and Tools.- 1.1.1 Multi-Objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges, by Carlos A. Coello Coello.- 1.1.2 Hybrid Optimization Algorithms and Hybrid Response Surfaces, by George S. Dulikravich and Marcelo J. Colaco.- 1.1.3 A genetic algorithm for a sensor device location problem, by Egidio D'Amato, Elia Daniele and Lina Mallozzi.- 1.1.4 The Role of Artificial Neural Networks in Evolutionary Optimization: A Review, by Mustapha Maarouf, Adriel Sosa, Blas Galvбn, David Greiner, Gabriel Winter, Mбximo Mendez and Ricardo Aguasca.- 1.1.5 Reliability-based Design Optimization with the Generalized Inverse Distribution Function, by Domenico Quagliarella, Giovanni Petrone and Gianluca Iaccarino.- 1.2 Numerical Methods and Tools.- 1.2.1 On the choice of surrogates for multilevel aircraft performance models, by Manon Bondouy, Sophie Jan, Serge Laporte and Christian Bes.- 1.2.2 Multi-objective design optimization using high-order statistics for CFD applications, by Pietro M. Congedo, Gianluca Geraci, Remi Abgrall and Gianluca Iaccarino.- 1.2.3 Extension of the One-Shot Method for Optimal Control with Unsteady PDEs, by Stefanie Gunther, Nicolas R. Gauger and Qiqi Wang.- 1.2.4 Adaptive Aerodynamic Design Optimization for Navier-Stokes using Shape Derivatives with Discontinuous Galerkin Methods, by Lena Kaland, Matthias Sonntag and Nicolas R. Gauger.- 1.2.5 Optimal Flow Control and Topology Optimization Using the Continuous Adjoint Method in Unsteady Flows, by Ioannis S. Kavvadias, George K. Karpouzas, Evangelos M. Paoutsis-Kiachagias, Dimitris I. Papadimitrou and Kyriakos C. Giannakoglou.- Part 2: Engineering Design and Societal Applications.- 2.1 Turbomachinery.- 2.1.1 Design optimization of the Primary Pump of a Nuclear Reactor, by Tom Verstraete and Lasse Mueller.- 2.1.2 Direct 3D Aerodynamic Optimization of Turbine Blades with GPU-accelerated CFD, by Philipp Amtsfeld, Dieter Bestle and Marcus Meyer.- 2.1.3 Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization, by Gianluca Badjan, Carlo Poloni, Andrew Pike and Nadir Ince.- 2.1.4 Robust Aerodynamic Design Optimization of Horizontal Axis Wind Turbine Rotors, by Marco Caboni, Edmondo Minisci and Michele Sergio Campobaso.- 2.1.5 Horizontal axis hydroturbine shroud airfoil optimization, by Elia Daniele, Elios Ferrauto and Domenico P. Coiro.- 2.1.6 Parametric Blending and FE-Optimization of a Compressor Blisk Test Case, by Kai Karger and Dieter Bestle.- 2.1.7 Modular Automated Aerodynamic Compressor Design Process, by Fiete Poehlmann, Dieter Bestle, Peter Flassig and Michиl Hinz.- 2.1.8 Design-Optimization of a Compressor Blading on a GPU Cluster, by Konstantinos T. Tsiakas, Xenofon S. Trompoukis, Varvara G. Asouti and Kyriakos C. Giannakoglou.- 2.2 Structures, Materials and Civil Engineering.- 2.2.1 Immune and Swarm Optimization of Structures, by Tadeusz Burczyński, Arkadiusz Poteralski and Miroslaw Szczepanik.- 2.2.2 Investigation of three genotypes for mixed variable evolutionary optimization, by Rajan Filomeno Coelho, Manyu Xiao, Aurore Guglielmetti, Manuel Herrera and Weihong Zhang.- 2.2.3 A Study of Nash-Evolutionary Algorithms for Reconstruction Inverse Problems in Structural Engineering, by David Greiner, Jacques Pйriaux, Josй Marнa Emperador, Blas Galvбn and Gabriel Winter.- 2.2.4 A comparative study on design optimization of polygonal and Bйzier curve-shaped thin noise barriers using dual BEMformulation, by Rayco Toledo, Juan J. Aznбrez, Orlando Maeso and David Greiner.- 2.2.5 A Discrete Adjoint Approach For Trailing-Edge Noise Minimization using Porous Material, by Beckett Y. Zhou, Nicolas R. Gauger, Seong R. Koh and Wolfgang Schrцder.- 2.3 Aeronautics and Astronautics.- 2.3.1 Conceptual Design of Single-Stage Launch Vehicle with Hybrid Rocket Engine Using Desi
Описание: This book contains thirty-five selected papers presented at the International Conference on Evolutionary and Deterministic Methods for Design, Optimization and Control with Applications to Industrial and Societal Problems (EUROGEN 2017). This was one of the Thematic Conferences of the European Community on Computational Methods in Applied Sciences (ECCOMAS).Topics treated in the various chapters reflect the state of the art in theoretical and numerical methods and tools for optimization, and engineering design and societal applications. The volume focuses particularly on intelligent systems for multidisciplinary design optimization (mdo) problems based on multi-hybridized software, adjoint-based and one-shot methods, uncertainty quantification and optimization, multidisciplinary design optimization, applications of game theory to industrial optimization problems, applications in structural and civil engineering optimum design and surrogate models based optimization methods in aerodynamic design.
Mark H.A. Davis introduced the Piecewise-Deterministic Markov Process (PDMP) class of stochastic hybrid models in an article in 1984. Today it is used to model a variety of complex systems in the fields of engineering, economics, management sciences, biology, Internet traffic, networks and many more. Yet, despite this, there is very little in the way of literature devoted to the development of numerical methods for PDMDs to solve problems of practical importance, or the computational control of PDMPs.
This book therefore presents a collection of mathematical tools that have been recently developed to tackle such problems. It begins by doing so through examples in several application domains such as reliability. The second part is devoted to the study and simulation of expectations of functionals of PDMPs. Finally, the third part introduces the development of numerical techniques for optimal control problems such as stopping and impulse control problems.
Описание: This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-and coming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include: optimization in dynamic environments, multi-objective bilevel programming, handling high dimensionality under many objectives, and evolutionary multitasking. In addition to theory and methodology, this book describes several real-world applications from various domains, which will expose the readers to the versatility of evolutionary multi-objective optimization.
Preface.- Part 1: Theoretical and Numerical Methods and Tools for Optimization.- 1.1 Theoretical Methods and Tools.- 1.1.1 Multi-Objective Evolutionary Algorithms in Real-World Applications: Some Recent Results and Current Challenges, by Carlos A. Coello Coello.- 1.1.2 Hybrid Optimization Algorithms and Hybrid Response Surfaces, by George S. Dulikravich and Marcelo J. Colaco.- 1.1.3 A genetic algorithm for a sensor device location problem, by Egidio D'Amato, Elia Daniele and Lina Mallozzi.- 1.1.4 The Role of Artificial Neural Networks in Evolutionary Optimization: A Review, by Mustapha Maarouf, Adriel Sosa, Blas Galvбn, David Greiner, Gabriel Winter, Mбximo Mendez and Ricardo Aguasca.- 1.1.5 Reliability-based Design Optimization with the Generalized Inverse Distribution Function, by Domenico Quagliarella, Giovanni Petrone and Gianluca Iaccarino.- 1.2 Numerical Methods and Tools.- 1.2.1 On the choice of surrogates for multilevel aircraft performance models, by Manon Bondouy, Sophie Jan, Serge Laporte and Christian Bes.- 1.2.2 Multi-objective design optimization using high-order statistics for CFD applications, by Pietro M. Congedo, Gianluca Geraci, Remi Abgrall and Gianluca Iaccarino.- 1.2.3 Extension of the One-Shot Method for Optimal Control with Unsteady PDEs, by Stefanie Gunther, Nicolas R. Gauger and Qiqi Wang.- 1.2.4 Adaptive Aerodynamic Design Optimization for Navier-Stokes using Shape Derivatives with Discontinuous Galerkin Methods, by Lena Kaland, Matthias Sonntag and Nicolas R. Gauger.- 1.2.5 Optimal Flow Control and Topology Optimization Using the Continuous Adjoint Method in Unsteady Flows, by Ioannis S. Kavvadias, George K. Karpouzas, Evangelos M. Paoutsis-Kiachagias, Dimitris I. Papadimitrou and Kyriakos C. Giannakoglou.- Part 2: Engineering Design and Societal Applications.- 2.1 Turbomachinery.- 2.1.1 Design optimization of the Primary Pump of a Nuclear Reactor, by Tom Verstraete and Lasse Mueller.- 2.1.2 Direct 3D Aerodynamic Optimization of Turbine Blades with GPU-accelerated CFD, by Philipp Amtsfeld, Dieter Bestle and Marcus Meyer.- 2.1.3 Evaluation of Surrogate Modelling Methods for Turbo-Machinery Component Design Optimization, by Gianluca Badjan, Carlo Poloni, Andrew Pike and Nadir Ince.- 2.1.4 Robust Aerodynamic Design Optimization of Horizontal Axis Wind Turbine Rotors, by Marco Caboni, Edmondo Minisci and Michele Sergio Campobaso.- 2.1.5 Horizontal axis hydroturbine shroud airfoil optimization, by Elia Daniele, Elios Ferrauto and Domenico P. Coiro.- 2.1.6 Parametric Blending and FE-Optimization of a Compressor Blisk Test Case, by Kai Karger and Dieter Bestle.- 2.1.7 Modular Automated Aerodynamic Compressor Design Process, by Fiete Poehlmann, Dieter Bestle, Peter Flassig and Michиl Hinz.- 2.1.8 Design-Optimization of a Compressor Blading on a GPU Cluster, by Konstantinos T. Tsiakas, Xenofon S. Trompoukis, Varvara G. Asouti and Kyriakos C. Giannakoglou.- 2.2 Structures, Materials and Civil Engineering.- 2.2.1 Immune and Swarm Optimization of Structures, by Tadeusz Burczyński, Arkadiusz Poteralski and Miroslaw Szczepanik.- 2.2.2 Investigation of three genotypes for mixed variable evolutionary optimization, by Rajan Filomeno Coelho, Manyu Xiao, Aurore Guglielmetti, Manuel Herrera and Weihong Zhang.- 2.2.3 A Study of Nash-Evolutionary Algorithms for Reconstruction Inverse Problems in Structural Engineering, by David Greiner, Jacques Pйriaux, Josй Marнa Emperador, Blas Galvбn and Gabriel Winter.- 2.2.4 A comparative study on design optimization of polygonal and Bйzier curve-shaped thin noise barriers using dual BEMformulation, by Rayco Toledo, Juan J. Aznбrez, Orlando Maeso and David Greiner.- 2.2.5 A Discrete Adjoint Approach For Trailing-Edge Noise Minimization using Porous Material, by Beckett Y. Zhou, Nicolas R. Gauger, Seong R. Koh and Wolfgang Schrцder.- 2.3 Aeronautics and Astronautics.- 2.3.1 Conceptual Design of Single-Stage Launch Vehicle with Hybrid Rocket Engine Using Desi
Описание: 1. Keynote: Risk, Optimization and Meanfield Type Control, by Olivier Pironneau and Mathieu Lauriиre.- 2. Surrogate-Based Optimization in Aerodynamic Design.- A Review of Surrogate Modeling Techniques for Aerodynamic Analysis and Optimization: Current Limitations and Future Challenges in Industry, by Raul Yondo, Kamil Bobrowski, Esther Andrйs and Eusebio Valero.- Constrained Single-Point Aerodynamic Shape Optimization of the DPW-W1 wing through Evolutionary Programming and Support Vector Machines, by E. Andrйs-Pйrez, D. Gonzбlez-Juбrez, M. J. Martin-Burgos, L. Carro-Calvo.- Enabling of Large Scale Aerodynamic Shape Optimization through POD-based Reduced-Order Modeling and Free Form Deformation, by A. Scardigli, R. Arpa, A. Chiarini and H. Telib.- Application of Surrogate-based Optimization Techniques to Aerodynamic Design Cases, by Emiliano Iuliano and Domenico Quagliarella.- Efficient Global Optimization method for multipoint airfoil design, by Davide Cinquegrana and Emiliano Iuliano.- 3. Adjoint Methods for Steady and Unsteady Optimization.- Checkpointing with time gaps for unsteady adjoint CFD, by Jan Christian Hueckelheim and Jens-Dominik Mueller.- Shape Optimization ofWind Turbine Blades using the Continuous Adjoint Method and Volumetric NURBS on a GPU Cluster, by Konstantinos T. Tsiakas, Xenofon S. Trompoukis, Varvara G. Asouti and Kyriakos C. Giannakoglou.- Aerodynamic Shape Optimization Using the Adjoint-based Truncated Newton Method, by Evangelos M. Papoutsis-Kiachagias, Mehdi Ghavami Nejad, and Kyriakos C. Giannakoglou.- Application of the adjoint method for the reconstruction of the boundary condition in unsteady shallow water flow simulation, by Asier Lacasta, Daniel Caviedes-Voulliиme and Pilar Garcнa-Navarro.- Aerodynamic Optimization of Car Shapes using the Continuous Adjoint Method and an RBF Morpher, by E.M. Papoutsis-Kiachagias, S. Porziani, C. Groth, M.E. Biancolini, E. Costa and K.C. Giannakoglou.- 4. Holistic Optimization in Marine Design.- Upfront CAD - Parametric modeling techniques for shape optimization, by S. Harries, C. Abt and M. Brenner.- Simulation-based Design Optimization by Sequential Multi-criterion Adaptive Sampling and Dynamic Radial Basis Functions, by Matteo Diez, Silvia Volpi, Andrea Serani, Frederick Stern and Emilio F. Campana.- Application of Holistic Ship Optimization in Bulkcarrier Design and Operation, by Lampros Nikolopoulos, Evangelos Boulougouris.- 5. Game Strategies Combined with Evolutionary Computation.- Designing Networks in Cooperation with ACO, by E. D'Amato, E. Daniele and L. Mallozzi.- Augmented Lagrangian approach for constrained potential Nash games, by Lina Mallozzi and Domenico Quagliarella.- A Diversity Dynamic Territory Nash Strategy in Evolutionary Algorithms: Enhancing Performances in Reconstruction Problems in Structural Engineering, by David Greiner, Jacques Pйriaux, J.M. Emperador, B. Galvбn, G. Winter.- Interactive Inverse Modeling Based Multiobjective Evolutionary Algorithm, by Karthik Sindhya and Jussi Hakanen.- Multi-Disciplinary Design Optimization of Air-breathing Hypersonic Vehicle Using Pareto Games and Evolutionary Algorithms, by Peng Wu, Zhili Tang, Jacques Periaux.- 6. Optimisation under Uncertainty.- Innovative methodologies for Robust Design Optimization with large number of uncertainties using modeFRONTIER, by Alberto Clarich, Rosario Russo.- A Novel Method for Inverse Uncertainty Propagation, by Xin Chen, ArturoMolina-Crist obal, Marin D. Guenov, Varun C. Datta, Atif Riaz.- Uncertainty Sources in the Baseline Configuration for Robust Design of a Supersonic Natural Laminar Flow Wing-Body, by Domenico Quagliarella and Emiliano Iuliano.- Robust Airfoil Design in the Context of Multi-Objective Optimization, by Lisa Kusch and Nicolas R. Gauger.- An alternative formulation for design under uncertainty, by F. Fusi and P. M. Congedo and G. Geraci and G. Iaccarino.- Polynomial Representation of Model Uncertainty in Dynamical
Описание: Preface.- PART I: Adjoint Methods for Optimisation, Mesh Adaptation and Uncertainty Quantification.- Gradient projection, constraints and surface regularization methods in adjoint shape optimization, by Pavlos P. Alexias and Eugene De Villiers.- Adjoint Shape Optimisation using model Boundary Representation, by E. Andres-Perez et al..- CAD and Adjoint based Multipoint Optimization of an Axial Turbine Profile, by Ismael Sanchez Torreguitart, Tom Verstraete, and Lasse Mueller.- A Comparative Study of Two Different CAD-Based Mesh Deformation Methods for Structural Shape Optimization, by Marc Schwalbach, Tom Verstraete, Jens-Dominik Miller, and Nicolas R. Gauger.- Node-based Adjoint Surface Optimization of U-bend duct for pressure loss reduction, by Giacomo Alessi, Lilla Koloszar, Tom Verstraete, and J. van Beeck.- On the Properties of Solutions of the 2D Adjoint Euler Equations, by Carlos Lozano.- Finite Transformation Rigid Motion Mesh Morpher, by Athanasios G. Liatsikouras, Guillaume Pierrot, Gabriel Fougeron, and George S. Eleftheriou.- The Unsteady Continuous Adjoint Method Assisted by the Proper Generalized Decomposition Method, by V. S. Papageorgiou, K. D. Samouchos and K. C. Giannakoglou.- A Two-Step Mesh Adaptation Tool Based on RBF with application to turbomachinery Optimization Loops, by Flavio Gagliardi, Konstantinos T. Tsiakas and Kyriakos C. Giannakoglou.- Adjoint-based Aerodynamic Optimisation of Wing Shape Using Non-Uniform Rational B-splines, by Xingchen Zhang, Rejish Jesudasan and Jens-Dominik Mьller.- PART II: Surrogate-assisted Optimization of Real World problems.- A comparative evaluation of surrogate models for transonic wing shape optimization, by Emiliano Iuliano.- Study of the influence of the initial a-priori training dataset size in the efficiency and convergence of surrogate-based evolutionary optimization, by Daniel Gonzalez Juarez and Esther Andres Perez.- Garteur AD/AG52: Surrogate-based global optimization methods in preliminary aerodynamic design, by E. Andres-Perez et al..- A Response Surface Based Strategy for Accelerated Compressor Map Computation, by Dmitrij Ivanov, Dieter Bestle, and Christian Janke.- Surrogate-Based Shape Optimization of the ERCOFTAC Centrifugal Pump Impeller, by Remo De Donno and Stefano Rebay and Antonio Ghidoni.- CFD based Design Optimization of a Cabinet Nitrogen Generator, by Bбrbara Arizmendi Gutiйrrez and Edmondo Minisci.- Delaunay-based global optimization in nonconvex domains defined by hidden constraints, by Shahrouz Ryan Alimo, Pooriya Beyhaghi, and Thomas R. Bewley.- PART III: Applications of optimization in engineering design automation.- Optimized Vehicle Dynamics Virtual Sensing using Metaheuristic Optimization and Unscented Kalman Filter, by Manuel Acosta and Stratis Kanarachos.- On Combinatorial Problem Representation based Ascent Assembly Design Optimization, by Michael Hellwig, Doris Entner, Thorsten Prante, Alexandru-Ciprian Zavoianu, Martin Schwarz, and Klara Fink.- On the Optimization of 2D Path Network Layouts in Engineering Designs via Evolutionary Computation Techniques, by Alexandru-Ciprian Ziivoianu, Susanne Saminger-Platz, Doris Entner, Thorsten Prante, Michael Hellwig, Martin Schwarz, and Klara Fink.- Taking Advantage of 3D Printing so as to Simultaneously Reduce Weight and Mechanical Bonding Stress, by Markus Schatz, Robert Schweikle, Christian Lausch, Michael Jentsch and Werner Konrad.- Interactive Optimization of Path Planning for a Robot Enabled by Virtual Commissioning, by Ruth Fleisch, Doris Entner, Thorsten Prante, Reinhard Pfefferkorn.- Box-Type Boom Design using Surrogate Modeling: Introducing an Industrial Optimization Benchmark, by Philipp Fleck, Doris Entner, Clemens Munzer, Michael Kommenda, Thorsten Prante, Martin Schwarz, Martin Hachl, and Michael Affenzeller.- Knowledge Objects Enable Mass-Individualization, by Joel Johansson and Fredrik Elgh.- Free-form Optimization of a Shell Structure with Curvature Constraint,
Описание: This IMA Volume in Mathematics and its Applications NONSMOOTH ANALYSIS AND GEOMETRIC METHODS IN DETERMINISTIC OPTIMAL CONTROL is based on the proceedings of a workshop that was an integral part of the 1992-93 IMA program on "Control Theory.
Автор: Christoph Kawan Название: Invariance Entropy for Deterministic Control Systems ISBN: 3319012878 ISBN-13(EAN): 9783319012872 Издательство: Springer Рейтинг: Цена: 6986.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph provides an introduction to the concept of invariance entropy, the central motivation of which lies in the need to deal with communication constraints in networked control systems.
Описание: Investigates different deterministic and stochastic error bounds of numerical analysis. This book considers worst case error bounds and their relation to the theory of n-widths. It studies special problems such approximation, optimization, and integration for different function classes. It compares adaptive and nonadaptive methods.
Описание: Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
Описание: Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
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