Metaheuristics
Metaheuristics exhibit desirable properties like simplicity, easy parallelizability, and ready applicability to different types of optimization problems. After a comprehensive introduction to the field, the contributed chapters in this book include explan
- PDF / 22,015,109 Bytes
- 501 Pages / 453.543 x 683.15 pts Page_size
- 1 Downloads / 237 Views
Metaheuristics
Metaheuristics
Patrick Siarry Editor
Metaheuristics
123
Editor Patrick Siarry Laboratory LiSSi (EA 3956) Université Paris-Est Créteil Créteil France
ISBN 978-3-319-45401-6 DOI 10.1007/978-3-319-45403-0
ISBN 978-3-319-45403-0
(eBook)
Library of Congress Control Number: 2016952499 © 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 The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Contents
1
2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Patrick Siarry 1.1 “Hard” Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Source of the Effectiveness of Metaheuristics . . . . . . . . . . . . . . . 1.2.1 Trapping of a “Classical” Iterative Algorithm in a Local Minimum . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Capability of Metaheuristics to Extract Themselves from a Local Minimum . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Principles of the Most Widely Used Metaheuristics . . . . . . . . . . 1.3.1 Simulated Annealing . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 The Tabu Search Method . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Genetic Algorithms and Evolutionary Algorithms . . . . . 1.3.4 Ant Colony Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.5 Other Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Extensions of Metaheuristics . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Adaptation for Problems with Continuous Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Multiobjective Optimization . . . . . . . . . . . . . . . . . . . . . 1.4.3 Hybrid Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
