Multi-Objective Optimization using Evolutionary Algorithms

by Kalyanmoy Deb

Overview

Evolutionary algorithms are relatively new, but very powerful techniques used to find solutions to many real-world search and optimization problems. Many of these problems have multiple objectives, which leads to the need to obtain a set of optimal solutions, known as effective solutions. It has been found that using evolutionary algorithms is a highly effective way of finding multiple effective solutions in a single simulation run.

• Comprehensive coverage of this growing area of research
• Carefully introduces each algorithm with examples and in-depth discussion
• Includes many applications to real-world problems, including engineering design and scheduling
• Includes discussion of advanced topics and future research
• Can be used as a course text or for self-study
• Accessible to those with limited knowledge of classical multi-objective optimization and evolutionary algorithms

The integrated presentation of theory, algorithms and examples will benefit those working and researching in the areas of optimization, optimal design and evolutionary computing. This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.

Product Details

ISBN-13:	9780471873396
Publisher:	Wiley
Publication date:	07/05/2001
Series:	Wiley Interscience Series in Systems and Optimization , #16
Edition description:	1 ED
Pages:	536
Product dimensions:	6.85(w) x 9.94(h) x 1.36(d)

About the Author

Kalyanmoy Deb is an Indian computer scientist. Since 2013, Deb has held the Herman E. & Ruth J. Koenig Endowed Chair in the Department of Electrical and Computing Engineering at Michigan State University, which was established in 2001.

Table of Contents

Foreword xv

Preface xvii

1 Prologue 1

2 Multi-Objective Optimization 13

3 Classical Methods 49

4 Evolutionary Algorithms 81

5 Non-Elitist Multi-Objective Evolutionary Algorithms 171

6 Elitist Multi-Objective Evolutionary Algorithms 239

7 Constrained Multi-Objective Evolutionary Algorithms 289

8 Salient Issues of Multi-Objective Evolutionary Algorithms 315

9 Applications of Multi-Objective Evolutionary Algorithms 447

10 Epilogue 481

References 489

Index 509

Editorial Reviews

"Deb's book is complete, eminently readable, and the coverage is scholarly and thorough. It is my pleasure and duty to urge you to buy this book, read it, use it and enjoy it." (David E. Goldberg, University of Illinois at Urbana-Champaign, USA)

"...discusses two multi-objective optimization procedures, namely the ideal procedure and the preference-based one." (Zentralblatt MATH, Vol. 970, 2001/20)

Excerpt from Preface: "...provides an extensive discussion on the principles of multi-objective optimization and on a number of classical approaches." (Mathematical Reviews, 2002)

"...As a survey, this book is exemplary and forms an essential resource for EMO researchers at the present time." (Siam Review, Vol.44, No.3, 2002)

"...a readable account of a topic of current interest in operational research." (Mathematika, No.48, 2001)

—n outstandingly well-organized and clearly written account of the subject? (The Mathematical Gazette, July 2003)

From the Publisher

This book provides an integrated treatment of the theory and carefully introduces each algorithm with examples and detailed explication. Applications to engineering, design, and scheduling are emphasized. Intended as a textbook or self-study guide, the book assumes limited prior knowledge of the topics, but is intended to be useful to researchers working on optimization, optimal design, and evolutionary computing. Deb teaches mechanical engineering at the Indian Institute of Technology. Annotation c. Book News, Inc., Portland, OR (booknews.com)