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Applications of multi-objec...
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 solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain
Monografía
monografia Rebiun22494810 https://catalogo.rebiun.org/rebiun/record/Rebiun22494810 m o d cr cn||||||||| 070423s2004 njua ob 001 0 eng d 2007297637 304072292 748207438 815743912 935228926 1064093113 1081216814 9812567798 electronic bk. ; Adobe Reader) 9789812567796 electronic bk. ; Adobe Reader) 1281880841 9781281880840 9812561064 9789812561060 UPCT u109133 EBL244549 eBook Library http://www.eblib.com EBLCP eng pn EBLCP OCLCQ OCLCO OCLCF OCLCQ IDEBK OCLCQ MERUC OCLCQ U3W ICG OCLCQ WYU VT2 DKC OCLCQ PBW bicssc Applications of multi-objective evolutionary algorithms editors, Carlos A. Coello Coello, Gary B. Lamont Hackensack, NJ World Scientific ©2004 Hackensack, NJ Hackensack, NJ World Scientific 1 online resource (xxvii, 761 pages) illustrations 1 online resource (xxvii, 761 pages) Text txt rdacontent computer c rdamedia online resource cr rdacarrier Advances in natural computation v. 1 Includes bibliographical references and index 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 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 solutions for high-dimensional real-world design applications. The book contains a large collection of MOEA applications from many researchers, and thus provides the practitioner with detailed algorithmic direction to achieve good results in their selected problem domain Combinatorial optimization Evolutionary computation Combinatorial optimization. Evolutionary computation. Genetische algoritmen. Electronic books Electronic book Coello Coello, Carlos A. Lamont, Gary B. Print version Applications of multi-objective evolutionary algorithms. Hackensack, NJ : World Scientific, ©2004 9789812561060 (OCoLC)58479902 Advances in natural computation v. 1