Applications Of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation) PDF
Preview Applications Of Multi-Objective Evolutionary Algorithms (Advances in Natural Computation)
Applications of Multi-Objective Evolutionary Algorithms ADVANCES IN NATURAL COMPUTATION Series Editor: Xin Yao (The University of Birmingham, UK) Published Vol. 2: Recent Advances in Simulated Evolution and Learning Eds: Kay Chen Tan, Meng Hiot Lint, Xin Yao & Lipo Wang Applications of Multi-Objective Evolutionary Algorithms A d v a n c e s I n N a t u r a l C o m p u t a t i o n - V o l . 1 editors Carlos A Coello Coello (CINVESTAV-IPN, Mexico) Gary B Lamont (Air Force Institute of Technology, Wright-Patterson AFB, USA) World Scientific N E W J E R S E Y • L O N D O N • S I N G A P O R E • B E I J I N G • S H A N G H A I • H O N G K O N G • T A I P E I • C H E N N A I Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library. APPLICATIONS OF MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS Advances in Natural Computation — Vol. 1 Copyright © 2004 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher. For photocopying of material in this volume, please pay a copying fee through the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to photocopy is not required from the publisher. ISBN 981-256-106-4 Printed in Singapore by World Scientific Printers (S) Pte Ltd FOREWORD Computer science is playing an increasingly important role in many other fields, such as biology, physics, chemistry, economics and sociology. Many developments in these classical fields rely crucially on advanced comput- ing technologies. At the same time, these fields have inspired new ideas and novel paradigms of computing, such as evolutionary, neural, molecu- lar and quantum computation. Natural computation refers to the study of computational systems that use ideas and draw inspiration from natural systems, whether they be biological, ecological, molecular, neural, quantum or social. World Scientific Publishing Co. publishes an exciting book series on "Advances in Natural Computation." The series aims at serving as a cen- tral source of reference for the theory and applications of natural compu- tation, establishing the state of the art, disseminating the latest research discoveries, and providing potential textbooks to senior undergraduate and postgraduate students. The series publishes monographs, edited volumes and lecture notes on a wide range of topics in natural computation. Ex- amples of possible topics include, but are not limited to, evolutionary al- gorithms, evolutionary games, evolutionary economics, evolvable hardware, neural networks, swarm intelligence, quantum computation, molecular com- putation, ecological informatics, etc. This volume edited by Drs. Carlos A. Coello Coello and Gary B. Lam- ont represents an excellent start to this leading book series. It addresses a fast growing field of multi-objective optimization, especially its applications in many disciplines, from engineering design to spectroscopic data analysis, from groundwater monitor to regional planning, from autonomous vehicle navigation to polymer extrusion, and from bioinformatics to computational V vi Foreword a finance. It complements the journal papers beautifully and should be on the bookshelf of anyone who is interested in multi-objective optimization and its diverse applications. The two editors of this volume are both leading experts on evolutionary multi-objective optimization. They have done an outstanding job in putting together the most comprehensive book on the applications of evolutionary multi-objective optimization. I hope all readers will enjoy this volume as much as I do! The upcoming volume in this book series on "Recent Advances in Sim- ulated Evolution and Learning" will appear soon. If you are interested in writing or editing a volume for this book series, please get in touch with the Series Editor. Xin Yao Series Editor Advances in Natural Computation August 2004 a Carlos A. Coello Coello, Special Issue on Evolutionary Multi-objective Optimization, IEEE Transactions on Evolutionary Computation, 7(2), April 2003. PREFACE The intent of this book is to present a variety of multi-objective problems (MOPs) which have been solved using multi-objective evolutionary algo- rithms (MOEAs). Due to obvious space constraints, the set of applications included in the book is relatively small. However, the editors believe that such a set is representative of the current trends among both researchers and practitioners across many disciplines. This book aims not only to present a representative sampling of real- world MOPs currently being tackled by practitioners, but also to provide some insights into the different aspects related to the use of MOEAs in real-world applications. The reader should find the material particularly useful in analyzing the pragmatic (and sometimes peculiar) point of view of each contributor regarding how to choose a certain MOEA and how to validate the results using metrics and statistics. Another aspect that is worth addressing is the limited variety of MOEAs adopted throughout this book which is not as diverse as those presented in the literature. This indicates a certain degree of maturity within this research community, and at the same time defines some important cur- rent trends among practitioners. By reading the chapters, it is evident that certain MOEAs that some researchers in the field might consider "old- fashioned" (e.g., the Niched Pareto Genetic Algorithm) continue to be used by various practitioners. At the same time, it is evident that other "mod- ern" MOEAs (e.g., the Non-dominated Sorting Genetic Algorithm II) with available software are becoming increasingly popular. As MOEA software evolves and each incorporates an increasingly larger variety of operators, generic MOEA software should be available. For example, such software is currently being integrated into various optimization packages incorporat- ing a variety of search techniques. Of course, the MOEA discipline contin- vii viii Preface ues to evolve more sophisticated variants, hybridization techniques, unique methodologies depending on the problem domain, and use of efficient par- allel computation, with application to an increasing broader class of high- dimensional complex problems. The spectrum of real-world optimization MOPs dealt with in this book include, among others, aircraft design, robot planning, identification of in- teresting qualitative features in biological sequences, circuit design, pro- duction system control, city planning, ecological system management, and bioremediation of chemical pollution. Thus the organization of the book is structured around engineering, biology, chemistry, physics, and manage- ment disciplines. Throughout this book, the reader should find not only problems with different degrees of complexity, but also with different prac- tical requirements, user constraints, and a variety of MOEA solution ap- proaches. We would like to thank all the contributors for providing their insights regarding the use of MOEAs in solving real-world multi-objective problems. Without their serious consideration, contemplation, and devoted efforts in general, the discipline of MOEAs would not have evolved as well as this book. Such activity makes MOEAs a viable approach in finding effective and efficient solutions to complex MOPs. Observe that the contributors are from many countries reflecting the international interest in MOEA appli- cations and the interdisciplinary nature of optimization research. As indicated, this book presents a collection of MOEA applications which provide the professional and the practitioner with direction to achiev- ing "good" results in their selected problem domain. For the beginner, the Introductory chapter and the variety of MOEA application chapters should provide an understanding of generic MOPs and MOEA parameter and op- erator selection leading to acceptable results. For the expert, the variety of MOP applications generates a wider understanding of MOEA operator selection and insight to the path leading to problem solutions. Additional applications and theoretical MOEA papers can be found at the Evolutionary Multi-Objective Optimization (EMOO) Repository in- ternet web site at http://delta.cs.cinvestav.mx/~ccoello/EMOO/with mirrors at http://www.lania.mx/~ccoello/EMOO/ and at http://neo.lcc.uma.es/emoo/. As of mid 2004, the EMOO Repository Preface ix contained over 1700 bibliographic references, including more than 100 PhD theses, and over 1000 conference papers and 400 journal papers. However, the EMOO Repository is continually being updated. There is not only a large collection of bibliographic references (many of them electronically available), but also contains public-domain MOP and MOEA software and sample test problems as well as some other useful information which allows one to start working in this exciting research field. The general organization of the book is based on the types of applica- tions considered. Chapter 1 provides some preliminary material intended for those not familiar with the basic concepts and terminology adopted in evolutionary multi-objective optimization. This first chapter also provides a brief description of each of the other 29 chapters that integrate this book. These 29 chapters are divided in four parts. The first part is the largest and it consists of engineering applications (e.g., civil, mechanical, aeronautical, and chemical engineering, among others). This first part includes chapters 2 to 13. The second part consists of scientific applications (e.g., computer science, bioinformatics and physics, among others) and includes chapters 14 to 19. The third part consists of industrial applications (e.g., design, man- ufacture, packing and scheduling, among others) and includes chapters 20 to 24. The fourth and last part consists of miscellaneous applications such as data mining, finance and management. This last part includes chapters 25 to 30. The first editor gratefully acknowledges the support obtained from CINVESTAV-IPN and from the NSF-CONACyT project 42435-Y. He also thanks Dr. Luis Gerardo de la Fraga for his continuous support and Erika Hernandez Luna for her valuable help during the preparation of this book. The second author acknowledges the support of his graduate students in- cluding Rick Day and Mark Kleeman. We also acknowledge the use of an academic license of the Word2Tex™ converter (developed by Chikrii Soft- lab) to convert some of the chapters submitted in MS Word™ to I^TfrjX 2E, which is the tool that we adopted to process the entire manuscript. The editors thank Steven Patt, from World Scientific, who was very professional, incredibly helpful and always replied promptly to all of the editors' queries. The editors also thank Prof. Xin Yao for deciding to in- clude this book within his Advances in Natural Computation series.
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