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Hybrid Metaheuristics an Emerging Approach to Optimization.Pdf Christian Blum, Maria Jose´ Blesa Aguilera, Andrea Roli and Michael Sampels (Eds.) Hybrid Metaheuristics Studies in Computational Intelligence, Volume 114 Editor-in-chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: [email protected] Further volumes of this series can be found on our Vol. 104. Maria Virvou and Lakhmi C. Jain (Eds.) homepage: springer.com Intelligent Interactive Systems in Knowledge-Based Environment, 2008 Vol. 93. Manolis Wallace, Marios Angelides and Phivos ISBN 978-3-540-77470-9 Mylonas (Eds.) Vol. 105. Wolfgang Guenthner Advances in Semantic Media Adaptation and Enhancing Cognitive Assistance Systems with Inertial Personalization, 2008 Measurement Units, 2008 ISBN 978-3-540-76359-8 ISBN 978-3-540-76996-5 Vol. 94. Arpad Kelemen, Ajith Abraham and Yuehui Chen ¨ (Eds.) Vol. 106. Jacqueline Jarvis, Dennis Jarvis, Ralph Ronnquist Computational Intelligence in Bioinformatics, 2008 and Lakhmi C. Jain (Eds.) ISBN 978-3-540-76802-9 Holonic Execution: A BDI Approach, 2008 ISBN 978-3-540-77478-5 Vol. 95. Radu Dogaru Systematic Design for Emergence in Cellular Nonlinear Vol. 107. Margarita Sordo, Sachin Vaidya and Lakhmi C. Jain Networks, 2008 (Eds.) ISBN 978-3-540-76800-5 Advanced Computational Intelligence Paradigms in Healthcare - 3, 2008 Vol. 96. Aboul-Ella Hassanien, Ajith Abraham and Janusz ISBN 978-3-540-77661-1 Kacprzyk (Eds.) Computational Intelligence in Multimedia Processing: Vol. 108. Vito Trianni Recent Advances, 2008 Evolutionary Swarm Robotics, 2008 ISBN 978-3-540-76826-5 ISBN 978-3-540-77611-6 Vol. 97. Gloria Phillips-Wren, Nikhil Ichalkaranje and Vol. 109. Panagiotis Chountas, Ilias Petrounias and Janusz Lakhmi C. Jain (Eds.) Kacprzyk (Eds.) Intelligent Decision Making: An AI-Based Approach, 2008 Intelligent Techniques and Tools for Novel System ISBN 978-3-540-76829-9 Architectures, 2008 Vol. 98. Ashish Ghosh, Satchidananda Dehuri and Susmita ISBN 978-3-540-77621-5 Ghosh (Eds.) Vol. 110. Makoto Yokoo, Takayuki Ito, Minjie Zhang, Multi-Objective Evolutionary Algorithms for Knowledge Juhnyoung Lee and Tokuro Matsuo (Eds.) Discovery from Databases, 2008 Electronic Commerce, 2008 ISBN 978-3-540-77466-2 ISBN 978-3-540-77808-0 Vol. 99. George Meghabghab and Abraham Kandel Search Engines, Link Analysis, and User’s Web Behavior, Vol. 111. David Elmakias (Ed.) 2008 New Computational Methods in Power System Reliability, ISBN 978-3-540-77468-6 2008 ISBN 978-3-540-77810-3 Vol. 100. Anthony Brabazon and Michael O’Neill (Eds.) Natural Computing in Computational Finance, 2008 Vol. 112. Edgar N. Sanchez, Alma Y. Alan´ıs and Alexander ISBN 978-3-540-77476-1 G. Loukianov Vol. 101. Michael Granitzer, Mathias Lux and Marc Spaniol Discrete-Time High Order Neural Control: Trained with (Eds.) Kalman Filtering, 2008 Multimedia Semantics - The Role of Metadata, 2008 ISBN 978-3-540-78288-9 ISBN 978-3-540-77472-3 Vol. 113. Gemma Bel-Enguix, M. Dolores Jimenez-Lopez and Vol. 102. Carlos Cotta, Simeon Reich, Robert Schaefer and Carlos Mart´ın-Vide (Eds.) Antoni Ligeza (Eds.) New Developments in Formal Languages and Applications, Knowledge-Driven Computing, 2008 2008 ISBN 978-3-540-77474-7 ISBN 978-3-540-78290-2 Vol. 103. Devendra K. Chaturvedi Vol. 114. Christian Blum, Maria Jose´ Blesa Aguilera, Andrea Soft Computing Techniques and its Applications in Electrical Roli and Michael Sampels (Eds.) Engineering, 2008 Hybrid Metaheuristics, 2008 ISBN 978-3-540-77480-8 ISBN 978-3-540-78294-0 Christian Blum Maria Jose´ Blesa Aguilera Andrea Roli Michael Sampels (Eds.) Hybrid Metaheuristics An Emerging Approach to Optimization 123 Dr. Christian Blum Dr. Andrea Roli ALBCOM DEIS, Campus of Cesena Dept. Llenguatges i Sistemes Informatics´ Alma Mater Studiorum Universitat Politecnica` de Catalunya Universita` di Bologna Jordi Girona 1-3 Omega 112, Via Venezia 52 Campus Nord I-47023 Cesena E-08034 Barcelona Italy Spain [email protected] [email protected] Dr. Maria Jose´ Blesa Aguilera Dr. Michael Sampels ALBCOM IRIDIA Dept. Llenguatges i Sistemes Informatics´ Universite´ Libre de Bruxelles Universitat Politecnica` de Catalunya Avenue Franklin Roosevelt 50, CP 194/6 Jordi Girona 1-3 Omega 213, B-1050 Brussels Campus Nord Belgium E-08034 Barcelona [email protected] Spain [email protected] ISBN 978-3-540-78294-0 e-ISBN 978-3-540-78295-7 Studies in Computational Intelligence ISSN 1860-949X Library of Congress Control Number: 2008921710 c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broad- casting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer-Verlag. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, 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. Cover design: Deblik, Berlin, Germany Printed on acid-free paper 987654321 springer.com For my parents Maria and Dieter (Christian Blum) For Christian and Marc (Mar´ıa J. Blesa) For Elisabetta, Raffaella and Paolo (Andrea Roli) For Astrid, Julian and Carlotta (Michael Sampels) Preface When facing complex new optimization problems, it is very natural to use rules of thumb, common sense, trial and error, which are called heuristics, in order to find possible answers. Such approaches are, at first sight, quite different from rigorous scientific approaches, which are usually based on characteriza- tions, deductions, hypotheses and experiments. It is common knowledge that many heuristic criteria and strategies, which are used to find good solutions for particular problems, share common aspects and are often independent of the problems themselves. In the computer science and artificial intelligence community, the term metaheuristic was created and is now well accepted for general techniques which are not specific to a particular problem. Genetic and evolutionary al- gorithms, tabu search, simulated annealing, iterated local search, ant colony optimization, scatter search, etc. are typical representatives of this generic term. Research in metaheuristics has been very active during the last decades, which is easy to understand, when looking at the wide spectrum of fascinating problems that have been successfully tackled and the beauty of the techniques, many of them inspired by nature. Even though many combinatorial optimiza- tion problems are very hard to solve optimally, the quality of the results obtained by somewhat unsophisticated metaheuristics is often impressive. This success record has motivated researchers to focus on why a given metaheuristic is successful, on which problem instance characteristics should be exploited and on which problem model is best for the metaheuristic of choice. Investigations on theoretical aspects have begun, and formal theories of the working of metaheuristics are being developed. Questions as to which metaheuristic is best for a given problem used to be quite common and, more prosaically, often led to a defensive attitude towards other metaheuristics. Finally, it also became evident that the concentration on a single meta- heuristic is rather restrictive for advancing the state of the art when tack- ling both academic and practical optimization problems. Examples showed that a skillful combination of metaheuristics with concepts originating from other types of algorithms for optimization can lead to more efficient behavior VIII Preface and greater flexibility. For example, the incorporation of typical operations research (OR) techniques, such as mathematical programming, into meta- heuristics may be beneficial. Also, the combination of metaheuristics with other techniques known from artificial intelligence (AI), such as constraint programming and data mining, can be fruitful. Nowadays, such a combina- tion of one metaheuristic with components from other metaheuristics or with techniques from AI and OR techniques is called a hybrid metaheuristic. The lack of a precise definition of the term hybrid metaheuristics is some- times subject to criticism. On the contrary, we believe that this relatively open definition is helpful, because strict borderlines between related fields of research often block creative research directions. A vital research community needs new ideas and creativity, not overly strict definitions and limitations. In 2004, we founded with the First International Workshop on Hybrid Metaheuristics (HM 2004) a series of annual workshops. These workshops have developed into a forum for researchers who direct their work towards hybrid algorithms that go beyond the scope of single metaheuristics. The growing interest in these workshops is an indication that questions regarding the proper integration of different algorithmic components and the adequate analysis of results can now emerge from the shadows. With this backdrop, it becomes evident that hybrid metaheuristics is now a part of experimental science and that its strong interdisciplinarity supports cooperation between researchers with different expertise. In the light
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