Applications of multi-objective evolutionary algorithms /

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This book presents an extensive variety of multi-objective problems across diverse disciplines, along with statistical solutions using multi-objective evolutionary algorithms (MOEAs). The topics discussed serve to promote a wider understanding as well as the use of MOEAs, the aim being to find good...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Coello Coello, Carlos A., Lamont, Gary B.
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hackensack, NJ : World Scientific, ©2004.
Colección:Advances in natural computation ; v. 1.
Temas:
Combinatorial optimization.
Evolutionary computation.
Optimisation combinatoire.
Réseaux neuronaux à structure évolutive.
Combinatorial optimization
Evolutionary computation
Genetische algoritmen.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • FOREWORD; PREFACE; CONTENTS; CHAPTER 1 AN INTRODUCTION TO MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS AND THEIR APPLICATIONS; 1.1. Introduction; 1.2. Basic Concepts; 1.3. Basic Operation of a MOEA; 1.4. Classifying MOEAs; 1.4.1. Aggregating Functions; 1.4.2. Population-Based Approaches; 1.4.3. Pareto-Based Approaches; 1.5. MOEA Performance Measures; 1.6. Design of MOEA Experiments; 1.6.1. Reporting MOEA Computational Results; 1.7. Layout of the Book; 1.7.1. Part I: Engineering Applications; 1.7.2. Part II: Scientific Applications; 1.7.3. Part III: Industrial Applications.
  • 1.7.4. Part IV: Miscellaneous Applications1.8. General Comments; References; CHAPTER 2 APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN ENGINEERING DESIGN; 2.1. Introduction; 2.2. Multi-Objective Evolutionary Algorithm; 2.2.1. Algorithms; 2.3. Examples; 2.3.1. Design of a Welded Beam; 2.3.2. Preliminary Design of Bulk Carrier; 2.3.3. Design of Robust Airfoil; 2.4. Summary and Conclusions; References; CHAPTER 3 OPTIMAL DESIGN OF INDUSTRIAL ELECTROMAGNETIC DEVICES: A MULTIOBJECTIVE EVOLUTIONARY APPROACH; 3.1. Introduction; 3.2. The Algorithms.
  • 3.2.1. Non-Dominated Sorting Evolution Strategy Algorithm (NSESA)3.3. Case Studies; 3.3.1. Shape Design of a Shielded Reactor; 3.3.2. Shape Design of an Inductor for Transverse-Flux-Heating of a Non-Ferromagnetic Strip; 3.4. Conclusions; References; CHAPTER 4 GROUNDWATER MONITORING DESIGN: A CASE STUDY COMBINING EPSILON DOMINANCE ARCHIVING AND AUTOMATIC PARAMETERIZATION ... ; 4.1. Introduction; 4.2. Prior Work; 4.3. Monitoring Test Case Problem; 4.3.1. Test Case Overview; 4.3.2. Problem Formulation; 4.4. Overview of the -NSGA-II Approach; 4.4.1. Searching with the NSGA-II; 4.4.2. Archive Update.
  • 4.4.3. Injection and Termination4.5. Results; 4.6. Discussion; 4.7. Conclusions; References; CHAPTER 5 USING A PARTICLE SWARM OPTIMIZER WITH A MULTI-OBJECTIVE SELECTION SCHEME TO DESIGN COMBINATIONAL LOGIC CIRCUITS; 5.1. Introduction; 5.2. Problem Statement; 5.3. Our Proposed Approach; 5.4. Use of a Multi-Objective Approach; 5.5. Comparison of Results; 5.5.1. Example 1; 5.5.2. Example 2; 5.5.3. Example 3; 5.5.4. Example 4; 5.5.5. Example 5; 5.5.6. Example 6; 5.6. Conclusions and Future Work; Acknowledgements; References.
  • CHAPTER 6 APPLICATION OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS IN AUTONOMOUS VEHICLES NAVIGATION6.1. Introduction; 6.2. Autonomous Vehicles; 6.2.1. Experimental Setup; 6.2.2. Vehicle Model; 6.2.3. Relative Sensor Models; 6.2.4. Absolute Sensor Models; 6.2.5. Simulation and Measurement of the Vehicle State; 6.2.6. Prediction of the Vehicle State; 6.3. Parameter Identification of Autonomous Vehicles; 6.3.1. Problem Formulation; 6.3.2. A General Framework for Searching Pareto-Optimal Solutions; 6.3.3. Selection of a Single Solution by CoGM; 6.4. Multi-Objective Optimization.

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