Hybrid Metaheuristics Powerful Tools for Optimization
This book explains the most prominent and some promising new, general techniques that combine metaheuristics with other optimization methods. A first introductory chapter reviews the basic principles of local search, prominent metaheuristics, and tree sea
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Christian Blum Günther R. Raidl
Hybrid Metaheuristics Powerful Tools for Optimization
Artificial Intelligence: Foundations, Theory, and Algorithms Series Editors Barry O’Sullivan, Cork, Ireland Michael Wooldridge, Oxford, United Kingdom
More information about this series at http://www.springer.com/series/13900
Christian Blum • Günther R. Raidl
Hybrid Metaheuristics Powerful Tools for Optimization
Christian Blum Dept. of Computer Science and Artificial Intelligence University of the Basque Country San Sebastian, Spain
Günther R. Raidl Algorithms and Data Structures Group Vienna University of Technology Vienna, Austria
ISSN 2365-3051 ISSN 2365-306X (electronic) Artificial Intelligence: Foundations, Theory, and Algorithms ISBN 978-3-319-30882-1 ISBN 978-3-319-30883-8 (eBook) DOI 10.1007/978-3-319-30883-8 Library of Congress Control Number: 2016939578 © Springer International Publishing Switzerland 2016 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer International Publishing AG Switzerland
To Gabi and Mar´ıa and our children J´ulia, Manuela, Marc, and Tobias. Without their love and support life would not be the same.
Preface
Research in metaheuristics for combinatorial optimization problems has lately experienced a noteworthy shift towards the hybridization of metaheuristics with other techniques for optimization. At the same time, the focus of research has changed from being rather algorithm-oriented to being more problem-oriented. Nowadays the focus is on solving a problem at hand in the best way possible, rather than promoting a certain metaheuristic. This has led to an enormously fruitful cross-fertilization of different areas of optimization, algorithmics, mathematical modeling, operations research, statistics, simulation, and other fields. This crossfertilization has resulted in a multitude of powerful hybrid algorithms that were obtained by co
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