Parmee Evolutionary and Adaptive Computing in Engineering Design

2001
ISBN: 978-1-4471-0273-1
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 286 Seiten, eBook

Following an introduction to the various techniques and examples of their routine application, this potential is explored through the introduction of various strategies that support searches across a far broader set of possible design solutions within time and budget constraints. Generic problem areas investigated include:

- design decomposition;

- whole-system design;

- multi-objective and constraint satisfaction;

- human-computer interaction;

- computational expense.

Appropriate strategies that help overcome problems often encountered when integrating computer-based techniques with complex, real-world design environments are described. A straightforward approach coupled with examples supports a rapid understanding of the manner in which such strategies can best be designed to handle the complexities of a particular problem.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

Parmee, Ian C.

Inhaltsverzeichnis

1.1 Setting the Scene.- 1.2 Why Evolutionary/Adaptive Computing?.- 1.3 The UK EPSRC Engineering Design Centres.- 1.4 Evolutionary and Adaptive Computing Integration.- 1.4.1 The Design Process.- 1.4.2 Routine, Innovative and Creative Design.- 1.4.3 Complementary Computational Intelligence Techniques.- 1.5 Generic Design Issues.- 1.6 Moving On.- 2. Established Evolutionary Search Algorithms.- 2.1 Introduction.- 2.2 A Brief History of Evolutionary Search Techniques.- 2.3 The Genetic Algorithm.- 2.3.1 The Simple Genetic Algorithm.- 2.3.2 Binary Mapping and the Schema Theorem.- 2.3.3 Real Number Representation.- 2.3.4 The Operators.- 2.3.5 Elitism and Exploitation versus Exploration.- 2.3.6 Self-adaptation.- 2.4 GA Variants.- 2.4.1 The CHC Genetic Algorithm.- 2.4.2 The EcoGA.- 2.4.3 The Structured Genetic Algorithm.- 2.4.4 The Breeder GA and the Messy GA.- 2.5 Evolution Strategies.- 2.6 Evolutionary Programming.- 2.7 Genetic Programming.- 2.8 Discussion.- 3. Adaptive Search and Optimisation Algorithms.- 3.1 Introduction.- 3.2 The Ant-colony Metaphor.- 3.3 Population-based Incremental Learning.- 3.4 Simulated Annealing.- 3.5 Tabu Search.- 3.6 Scatter Search.- 3.7 Discussion.- 4. Initial Application.- 4.1 Introduction.- 4.2 Applying the GA to the Shape Optimisation of a Pneumatic, Low-head, Hydropower Device.- 4.3 The Design ofGas Turbine Blade Cooling Hole Geometries.- 4.3.1 Introduction.- 4.3.2 Integrating the Cooling Hole Model with a Genetic Algorithm.- 4.3.3 Further Work.- 4.4 Evolutionary FIR Digital Filter Design.- 4.4.1 Introduction.- 4.4.2 Coding Using a Structured GA.- 4.4.3 Fitness Function.- 4.4.4 Results.- 4.5 Evolutionary Design of a Three-centred Concrete Arch Dam.- 4.6 Discussion.- 5. The Development of Evolutionary and Adaptive Search Strategies for Engineering Design.- 5.1 Introduction.- 5.2 Cluster-oriented Genetic Algorithms.- 5.3 The GAANT (GA-Ant) Algorithm.- 5.4 DRAM and HDRAM Genetic Programming Variants.- 5.5 Evolutionary and Adaptive Search Strategies for Constrained Problems.- 5.6 Evolutionary Multi-criterion Satisfaction.- 5.7 Designer Interaction within an Evolutionary Design Environment.- 5.8 Dynamic Shape Refinement and Injection Island Variants.- 5.9 Discussion.- 6. Evolutionary Design Space Decomposition.- 6. I Introduction.- 6.2 Multi-modal Optimisation.- 6.3 Cluster-oriented Genetic Algorithms.- 6.4 Application of vmCOGA.- 6.4.1 Two-dimensional Test Functions.- 6.4.2 Engineering Design Domains.- 6.4.3 Single-objective/Continuous Design Space.- 6.4.4 Multi-level , Mixed-parameter Design Space.- 6.5 Alternative COGA Structures.- 6.5.1 Introduction.- 6.5.2 The COGA Variants.- 6.5.3 Summary of Results.- 6.5.4 Search Space Sampling.- 6.5.5 The Dynamic Adaptive Filter.- 6.6 Agent-assisted Boundary Identification.- 6.7 Discussion.- 7. Whole-system Design.- 7.1 Introduction.- 7.1.1 Whole-system Design.- 7.1.2 Designer Requirement.- 7.1.3 Design Environments.- 7.2 Previous Related Work.- 7.3 The Hydropower System.- 7.3.1 The System.- 7.3.2 The Model.- 7.4 The Structured Genetic Algorithm.- 7.4.1 The Algorithm.- 7.4.2 Dual Mutation Strategies.- 7.4.3 stGA Results.- 7.5 Simplifying the Parameter Representation.- 7.6 Results and Discussion.- 7.7 Thermal Power System Redesign.- 7.7.1 Introduction.- 7.7.2 Problem Definition.- 7.7.3 A Hybrid GA-SLP Algorithm.- 7.7.4 The Design Application.- 7.8 Discussion.- 8. Variable-length Hierarchies and System Identification.- 8.1 Introduction.- 8.2 Improving Rolls Royce Cooling Hole Geometry Models.- 8.2.1 Introduction.- 8.2.2 Simple Curve and Surface Fitting.- 8.2.3 Evolving Formulae to Determine the Friction Factor in Turbulent Pipe Flow.- 8.2.4 Eddy Correlations for Laminar Two-dimensional Sudden Expansion Flows.- 8.3 Discussion of Initial Application.- 8.4 Further Development of the GP Paradigm.- 8.4.1 Development of Node Complexity Ratings.- 8.4.2 Constrained-complexity Crossover.- 8.4.3 Steady-state GP.- 8.4.4 Injection Mutation.- 8.5 Symbolic Regression with HDRAM-GP.- 8.6 Dual-agent Integration.- 8.7 Return to Engineering Applications.- 8.7.1 Introduction.- 8.7.2 Explicit Formula for Friction Factor In Turbulent Pipe Flow.- 8.7.3 Eddy Correlations for Laminar Two-dimensional Sudden Expansion Flows.- 8.7.4 Thermal Paint Jet Turbine Blade Data.- 8.8 Discussion.- 9. Evolutionary Constraint Satisfaction and Constrained Optimisation.- 9.1 Introduction.- 9.2 Dealing with Explicit Constraints.- 9.2.1 The Fault Coverage Test Code Generation Problem.- 9.2.2 The Inductive Genetic Algorithm.- 9.2.3 Application to the Problem.- 9.3 Implicit Constraints.- 9.4 Defining Feasible Space.- 9.4.1 Introduction.- 9.4.2 The Problem Domain.- 9.4.3 Fixing a Feasible Point.- 9.4.4 Creating a Feasible Subset.- 9.4.5 Establishing the Degree of Constraint Violation.- 9.4.6 Results and Discussion.- 9.5 Satisfying Constraint in the Optimisation of Thermal Power Plant Design.- 9.6 GA/Ant-colony Hybrid for the Flight Trajectory Problem.- 9.6.1 The Problem Domain.- 9.6.2 The Ant-colony Model for Continuous-space Search.- 9.6.3 A Hybrid Search Framework.- 9.7 Other Techniques.- 9.8 Discussion.- 10. Multi-objective Satisfaction and Optimisation.- 10.1 Introduction.- 10.2 Established Multi-objective Optimisation Techniques.- 10.2.1 Weighted-sum-based Optimisation.- 10.2.2 Lexicographic Order-based Optimisation.- 10.2.3 The Pareto Method.- 10.2.4 Pareto Examples.- 10.2.5 The Vector-evaluated Genetic Algorithm.- 10.2.6 Comparison of the Various Techniques.- 10.3 Interactive Approaches to Multi-objective Satisfaction/Optimisation.- 10.4 Qualitative Evaluation ofGA-generated Design Solutions.- 10.4.1 Introduction.- 10.4.2 The Design Model.- 10.4.3 Adaptive Restricted Tournament Selection.- 10.4.4 Assessing the Qualitative Fitness of High-performance Solutions.- 10.4.5 Knowledge Representation.- 10.4.6 Typical Results.- 10.4.7 Further Work.- 10.5 Cluster-oriented Genetic Algorithms for Multi-objective Satisfaction.- 10.6 Related Work and Further Reading.- 10.7 Discussion.- 11. Towards Interactive Evolutionary Design Systems.- 11.1 Introduction.- 11.2 System Requirements.- 11.3 The Design Environment and the IEDS.- 11.4 The Rule-based Preference Component.- 11.4.1 Introduction.- 11.4.2 Preferences.- 11.4.3 Example Application.- 11.5 The Co-evolutionary Environment.- 11.5.1 Introduction.- 11.5.2 Initial Methodology.- 11.5.3 The Range Constraint Map.- 11 .5.4 Sensitivity Analysis.- 11.5.5 Results.- 11.6 Combining Preferences with the Co-evolutionary Approach.- 11.7 Cluster-oriented Genetic Algorithm s as Information Gathering Processes.- 11.7.1 Introduction.- 11.7.2 Extraction and Processing of COGA-generated Data.- 11.8 Machine-based Agent Support.- 11.8.1 Introduction.- 11.8.2 Interface Agents.- 11.8.3 Communication Agents.- 11.8.4 Search Agents.- 11.8.5 Information Processing Agents.- 11.8.6 Negotiating Agents.- 11.9 Machine-based Design Space Modification.- 11.9.1 Introduction.- 11.9.2 The Developed EcoGA Framework.- 11.9.3 Determining Direction and Extent of Design Space Extension.- 11.10 Discussion.- 12. Population-based Search, Shape Optimisation and Computational Expense.- 12.1 Introduction.- 12.2 Parallel , Distributed and Co-evolutionary Strategies.- 12.3 Introducing the Problem and the Developed Strategies.- 12.4 The Evaluation Model.- 12.5 Initial Results.- 12.6 Dynamic Shape Refinement.- 12.6.1 Introduction.- 12.6.2 Stand-alone CHC and DSR CHC.- 12.7 The Injection Island GA.- 12.8 Dynamic Injection.- 12.9 Distributed Search Techniques.- 12.9.1 Introduction.- 12.9.2 Co-operative Search.- 12.10 Discussion.- 13. Closing Discussion.- 13.1 Introduction.- 13.2 Difficulties Facing Successful Integration ofEC with Engineering Design.- 13.3 Overview of the Techniques and Strategies Introduced.- 13.4 Final Remarks.- Appendix A. Some Basic Concepts.- References.