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Evolutionary Optimization and Game Strategies for Advanced Multi-Disciplinary Design : Applications to Aeronautics and UAV Design.

Author: Jacques Periaux; Felipe González; Dong Seop Chris Lee
Publisher: Dordrecht : Springer Netherlands, 2015.
Series: Intelligent systems, control and automation: science and engineering, 75.
Edition/Format:   eBook : Document : English : Online-ausgView all editions and formats
Summary:
Foreword; Preface; Contents; Chapter-1; Introduction; 1.1 Background; 1.2 Motivation; 1.3 Summary of Chapters; References; Chapter-2; Evolutionary Methods; 2.1 Overview; 2.2 Fundamentals of Evolutionary Algorithms (EAs); 2.3 Evolutionary Algorithms (EAs); 2.4 Benefits of EAs; 2.4.1 General Presentation of EAs Using Binary Coding; 2.4.2 Description of a Simple EA; 2.5 Mechanics of EAs; 2.5.1 Representation of
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Details

Genre/Form: Electronic books
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Jacques Periaux; Felipe González; Dong Seop Chris Lee
ISBN: 9789401795203 9401795207
OCLC Number: 910523478
Description: Online-Ressource (1 online resource (323 Seiten)).
Series Title: Intelligent systems, control and automation: science and engineering, 75.

Abstract:

Foreword; Preface; Contents; Chapter-1; Introduction; 1.1 Background; 1.2 Motivation; 1.3 Summary of Chapters; References; Chapter-2; Evolutionary Methods; 2.1 Overview; 2.2 Fundamentals of Evolutionary Algorithms (EAs); 2.3 Evolutionary Algorithms (EAs); 2.4 Benefits of EAs; 2.4.1 General Presentation of EAs Using Binary Coding; 2.4.2 Description of a Simple EA; 2.5 Mechanics of EAs; 2.5.1 Representation of Individuals; 2.6 Evolution Strategies (ESs); 2.7 Application of EAs to Constrained Problems; 2.8 Summary of Chapter; References; Chapter-3; Multi-Objective EAs And Game Theory.

3.1 Generalities3.2 Definition of A Mult- Objective Problem; 3.3 Cooperative Games: Pareto Optimality; 3.4 Competitive Games: Nash Equilibrium; 3.4.1 Definition of Nash Equilibrium; 3.4.2 Coupling Nash Games and GAs; 3.4.3 Generalization to N Nash Players; 3.5 Hierarchical Game: Stackelberg; 3.5.1 Coupling a Stackelberg Game with GAs; 3.6 Comparison of Analytical Solutions and Numerical Game Solutions for Solving a Two Mathematical Functions Minimisation Problem; 3.6.1 Analytical Solution; 3.6.2 Nash/Gas and Stackleberg/Gas Numerical Solutions; 3.7 Hybridized Games.

3.7.1 Algorithms for HAPMOEA and Hybridized Games3.8 Summary of Chapter 3; References; Chapter-4; Advanced Techniques for Evolutionary Algorithms (EAs); 4.1 Generalities; 4.2 Distributed and Parallel EAS; 4.3 Hierarchical EAS (HEAs); 4.4 Asynchronous Evolutionary Algorithmss (EAS); 4.5 Advanced Operators; 4.5.1 Covariance Matrix Adaptation (CMA); 4.5.2 Pareto Tournament Selection; 4.6 Advanced Games; 4.6.1 Virtual And Real Nash-Games; 4.6.2 Nash-Game and Hierarchical Asynchronous Parallel EAs (NASH-HAPEA).

4.6.3 Hybrid-Game Coupled with Single-Objective or Multi-Objective Evolutionary Algorithms4.7 Meta Model Assisted EAS; 4.8 Summary of Chapter 4; References; Chapter-5; Multidisciplinary Design Optimisation and Robust Design in Aerospace Systems; 5.1 Generalities; 5.2 Conceptual, Preliminary and Detailed Design; 5.3 Multi-Disciplinary Design Analysis (MDA) and Optimisation; 5.3.1 Definition; 5.3.2 Challenges and needs for MDO; 5.3.3 MDO Application Using Gradient-Based Methods; 5.4 Approaches to MDO; 5.4.1 Multi-Disciplinary Design Feasible (MDF); 5.4.2 Individual Discipline Feasible (IDF).

5.4.3 Collaborative Optimization (CO)5.4.4 Criteria and Performance of MDO Implementations; 5.5 Uncertainty Based Robust Design; 5.5.1 Robust/Uncertainty Method; 5.5.2 From Single-Objective to Multi-Objective Design Optimisation Using the Robust Design Method; 5.5.3 Robust Multi-Objective/Multi-Disciplinary Design Optimization; 5.6 Limitations of Traditional Optimisation Techniques for MDO and Robust Design; 5.6.1 MDO Using Traditional Method and Evolutionary Algorithms; 5.6.2 Advantages and Drawbacks of Robust Design; 5.7 Summary of Chapter; References; Chapter-6.

A Framework for Numerical Design and Optimization Algorithms.

Many complex aeronautical design problems can be formulated with efficient multi-objective evolutionary optimization methods and game strategies. This book describes the role of advanced innovative evolution tools in the solution, or the set of solutions of single or multi disciplinary optimization. These tools use the concept of multi-population, asynchronous parallelization and hierarchical topology which allows different models including precise, intermediate and approximate models with each node belonging to the different hierarchical layer handled by a different Evolutionary Algorithm.

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