[Studies in Computational Intelligence] Computational Intelligence in Reliability Engineering Volume 39 ||

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<ul><li><p>'REGORY,EVITIN%D</p><p>#OMPUTATIONAL)NTELLIGENCEIN2ELIABILITY%NGINEERING%VOLUTIONARY4ECHNIQUESIN2ELIABILITY!NALYSISAND/PTIMIZATION</p><p>3TUDIESIN#OMPUTATIONAL)NTELLIGENCE</p></li><li><p>Gregory Levitin (Ed.)Computational Intelligence in Reliability Engineering</p></li><li><p>Systems Research Institute Polish Academy of Sciencesul. Newelska 6 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl:</p><p>Further volumes of this seriescan be found on our homepage: springer.com</p><p>Vol. 23. M. Last, Z. Volkovich, A. Kandel (Eds.)Algorithmic Techniques for Data Mining, 2006ISBN 3-540-33880-2</p><p>Vol. 24. Alakananda Bhattacharya, Amit Konar,Ajit K. Mandal</p><p>2006</p><p>Victor Mitrana (Eds.)Recent Advances in Formal Languages and Applications, 2006,ISBN 3-540-33460-2</p><p>Vol. 25. Zolt n sik, Carlos Mart n-Vide,</p><p>Parallel and Distributed Logic Programming,</p><p>ISBN 3-540-33458-0</p><p>2006</p><p>Vol. 28. Brahim Chaib-draa, J rg P. M ller (Eds.)</p><p>ISBN 3-540-33875-6</p><p> Multiagent based Supply Chain Management,</p><p>Introduction to Data Mining and itsVol. 29. Sai Sumathi, S.N. Sivanandam</p><p>Vol. 30. Yukio Ohsawa, Shusaku Tsumoto (Eds.)</p><p>2006 Chance Discoveries in Real World Decision </p><p>Vol. 31. Ajith Abraham, Crina Grosan, Vitorino</p><p>Stigmergic Optimization,ISBN 3-540-34689-9</p><p>2006</p><p>Applications, 2006 ISBN 3-540-34689-9</p><p>Making,ISBN 3-540-34352-0</p><p>Ramos (Eds.)</p><p>Prof. Janusz Kacprzyk Editor-in-chief</p><p>2006Vol. 32. Akira HiroseComplex-Valued Neural Networks, ISBN 3-540-33456-4</p><p>Vol. 33. Martin Pelikan, Kumara Sastry, ErickCant-Paz (Eds.)</p><p>Modeling,Scalable Optimization via Probabilistic </p><p>ISBN 3-540-34953-7</p><p>2006</p><p>Vol. 34. Ajith Abraham, Crina Grosan, Vitorino Ramos (Eds.)Swarm Intelligence in Data Mining,ISBN 3-540-34955-3</p><p>Vol. 35. Ke Chen, Lipo Wang (Eds.)Trends in Neural Computation,ISBN 3-540-36121-9</p><p>Vol. 27. Vassilis G. KaburlasosTowards a Unified Modeling and Knowledge-</p><p>ISBN 3-540-34169-2</p><p>2006 (Eds.)Vol. 26. Nadia Nedjah, Luiza de Macedo Mourelle </p><p>Swarm Intelligent Systems, ISBN 3-540-33868-3</p><p>Representation based on Lattice Theory, 2006 </p><p>V</p><p>V</p><p>E-Service Intelligence,</p><p>ol. 38. Art Lew, Holger MauchDynamic Programming,ISBN 3-540-37013-7</p><p>Vol. 36. Ildar Batyrshin, Janusz Kacprzyk, LeonidSheremetor, Lotfi A. Zadeh (Eds.)</p><p>Making in Economics and Finance,ISBN 3-540-36244-4</p><p>ol. 37. Jie Lu, Da Ruan, Guangquan Zhang (Eds.)</p><p>Perception-based Data Mining and Decision2006</p><p>Studies in Computational Intelligence, Volume 39</p><p>Vol. 39. Gregory Levitin (Ed.)Computational Intelligence in Reliability</p><p>ISBN 3-540-37367-5</p><p>Vol. 22. N. Nedjah, E. Alba, L. de MacedoMourelle (Eds.)Parallel Evolutionary Computations, 2006 ISBN 3-540-32837-8</p><p>ISBN 3-540-37015-32007</p><p>2007</p><p> Engineering, 2007</p><p>2007</p><p>2006</p></li><li><p>123</p><p>Gregory Levitin (Ed.)</p><p>With 99 Figures and 64 Tables</p><p>Computational Intelligence in Reliability Engineering</p><p>Evolutionary Techniques in Reliability Analysis and Optimization</p></li><li><p>ISSN electronic edition: 1860-9503 </p><p>This work is subject to copyright. All rights are reserved, whether the whole or part of the mate-rial is concerned, specifically the rights of tran</p><p>j py g gj py g gslation, reprinting, reuse of illustrations, recita-</p><p>p</p><p>tion, broadcasting, reproduction on microfilm or in any other way, and storage in data banks.p y gp y g p gg</p><p>Duplication of this publicationg pg p</p><p>or parts thereof is permitted onyy</p><p>ly under the provisions of they gy g</p><p>German Copyright Law of Septemp pp</p><p>ber 9, 1965, in its current version, and permission for usep pp y pp</p><p>must always be obtained from Springer-Verlag. Violations are liable to prosecution under thepy g ppy g pp</p><p>German Copyright Law. y</p><p>Springer is a part of Springer Science+Business Media springer.com </p><p>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</p><p>g p gg p g pp</p><p>the relevant protective laws and regulations and therefore free for general use.p y pp y p</p><p>5 4 3 2 1 0 </p><p>Cover design: deblik, Berlin </p><p>ISSN print edition: 1860-949X </p><p>g</p><p>89/SPi</p><p>ISBN-10 3-540-37367-5 Springer Berlin Heidelberg New YorkISBN-13 978-3-540-37367-4 Springer Berlin Heidelberg New York</p><p>Typesetting by the editor and SPiPrinted on acid-ffree paper SPIN: 11418368</p><p>Dr. Gregory LevitinResearch E &amp;</p><p>The Israel Electric Corporation Ltd.</p><p>31000 HaifaIsraelE-mail: levitin@iec.co.il</p><p>Development Division </p><p>Library of Congress Control Number: 2006931548</p><p>PO Box 10</p><p>Springer-Verlag Berlin Heidelberg 2007</p></li><li><p> Preface </p><p>This two-volume book covers the recent applications of computational intelli-gence techniques in reliability engineering. Research in the area of computational intelligence is growing rapidly due to the many successful applications of these new techniques in very diverse problems. Computational Intelligence covers many fields such as neural networks, fuzzy logic, evolutionary computing, and their hybrids and derivatives. Many industries have benefited from adopting this technology. The increased number of patents and diverse range of products devel-oped using computational intelligence methods is evidence of this fact. These techniques have attracted increasing attention in recent years for solving many complex problems. They are inspired by nature, biology, statistical tech-niques, physics and neuroscience. They have been successfully applied in solving many complex problems where traditional problem-solving methods have failed. The book aims to be a repository for the current and cutting-edge applications of computational intelligent techniques in reliability analysis and optimization. In recent years, many studies on reliability optimization use a universal optimiza-tion approach based on metaheuristics. These metaheuristics hardly depend on the specific nature of the problem that is solved and, therefore, can be easily applied to solve a wide range of optimization problems. The metaheuristics are based on artificial reasoning rather than on classical mathematical programming. Their im-portant advantage is that they do not require any information about the objective function besides its values corresponding to the points visited in the solution space. All metaheuristics use the idea of randomness when performing a search, but they also use past knowledge in order to direct the search. Such algorithms are known as randomized search techniques. Genetic algorithms are one of the most widely used metaheuristics. They were inspired by the optimization procedure that exists in nature, the biological phe-nomenon of evolution. The first volume of this book starts with a survey of the con-tributions made to the optimal reliability design literature in the resent years. The next chapter is devoted to using the metaheuristics in multiobjective reliability optimization. The volume also contains chapters devoted to different applications of the genetic algorithms in reliability engineering and to combinations of this algorithm with other computational intelligence techniques. </p></li><li><p>The second volume contains chapters presenting applications of other metaheuris-tics such as ant colony optimization, great deluge algorithm, cross-entropy method and particle swarm optimization. It also includes chapters devoted to such novel methods as cellular automata and support vector machines. Several chapters pre-sent different applications of artificial neural networks, a powerful adaptive tech-nique that can be used for learning, prediction and optimization. The volume also contains several chapters describing different aspects of imprecise reliability and applications of fuzzy and vague set theory. All of the chapters are written by leading researchers applying the computational intelligence methods in reliability engineering. This two-volume book will be useful to postgraduate students, researchers, doc-toral students, instructors, reliability practitioners and engineers, computer scien-tists and mathematicians with interest in reliability. I would like to express my sincere appreciation to Professor Janusz Kacprzyk from the Systems Research Institute, Polish Academy of Sciences, Editor-in-Chief of the Springer series Studies in Computational Intelligence, for providing me with the chance to include this book in the series. I wish to thank all the authors for their insights and excellent contributions to this book. I would like to acknowledge the assistance of all involved in the review process of the book, without whose support this book could not have been suc-cessfully completed. I want to thank the authors of the book who participated in the reviewing process and also Prof. F. Belli, University of Paderborn, Germany, Prof. Kai-Yuan Cai, Beijing University of Aeronautics and Astronautics, Dr. M. Cepin, Jozef Stefan Institute, Ljubljana , Slovenia, Prof. M. Finkelstein, Univer-sity of the Free State, South Africa, Prof. A. M. Leite da Silva, Federal University of Itajuba, Brazil, Prof. Baoding Liu, Tsinghua University, Beijing, China, Dr. M. Muselli, Institute of Electronics, Computer and Telecommunication Engineering, Genoa, Italy, Prof. M. Nourelfath, Universit Laval, Quebec, Canada, Prof. W. Pedrycz, University of Alberta, Edmonton, Canada, Dr. S. Porotsky, FavoWeb, Is-rael, Prof. D. Torres, Universidad Central de Venezuela, Dr. Xuemei Zhang, Lu-cent Technologies, USA for their insightful comments on the book chapters. I would like to thank the Springer editor Dr. Thomas Ditzinger for his professional and technical assistance during the preparation of this book. </p><p>VI Preface </p><p>Gregory Levitin Haifa, Israel, 2006 </p></li><li><p> Contents </p><p>Way Kuo, Rui Wan ................................................................................................... 1 1.1 Introduction................................................................................................... 3 1.2 Problem Formulations .................................................................................. 6 1.3 Brief Review of Advances In P1-P4 .............................................................. 8 </p><p>1.3.1 Traditional Reliability-Redundancy Allocation Problem (P1).............. 8 1.3.1.1 Active and Cold-Standby Redundancy ...................................... 9 1.3.1.2 Fault-Tolerance Mechanism..................................................... 10 </p><p>1.3.2 Percentile Life Optimization Problem (P2) ......................................... 11 1.3.3 MSS Optimization (P3) ....................................................................... 12 1.3.4 Multi-Objective Optimization (P4)...................................................... 16 </p><p>1.4.1 Meta-Heuristic Methods ..................................................................... 19 1.4.1.1 Ant Colony Optimization Method ........................................... 19 1.4.1.2 Hybrid Genetic Algorithm ....................................................... 20 1.4.1.3 Tabu Search.............................................................................. 21 1.4.1.4 Other Meta-Heuristic Methods ................................................ 22 </p><p>1.4.2 Exact Methods..................................................................................... 22 1.4.3 Other Optimization Techniques .......................................................... 23 </p><p>1.5 Comparisons and Discussions of Algorithms Reported in Literature........ 25 1.6 Conclusions and Discussions...................................................................... 27 References......................................................................................................... 29 </p><p>2 Multiobjective Metaheuristic Approaches to Reliability Optimization Sadan Kulturel-Konak, Abdullah Konak, David W. Coit....................................... 37 </p><p>2.1 Introduction................................................................................................. 37 2.2 Metaheuristics and Multiobjective Optimization ....................................... 39 </p><p>2.2.1 Metaheuristics ..................................................................................... 39 2.2.2 Metaheuristic Approaches to Multiobjective Optimization................ 41 </p><p>2.3 Multiobjective Tabu Search and Reliability Optimization......................... 42 2.3.1 The Multinomial Tabu Search Algorithm to Solve Redundancy </p><p>Allocation Problem ............................................................................. 42 2.3.1.1 Multiobjective System Redundancy Allocation Problem........ 42 2.3.1.2 Multinomial Tabu Search Algorithm....................................... 43 2.3.1.3 Computational Experiments..................................................... 45 </p><p>2.4 Multiobjective Genetic Algorithms ............................................................ 47 2.4.1 Multiobjective GA Approaches to Reliability Optimization .............. 48 </p><p>1 Recent Advances in Optimal Reliability Allocation </p><p>1.4 Developments in Optimization Techniques ............................................... 18 </p></li><li><p>2.5 Multiobjective Optimization of Telecommunication Networks Considering Reliability .............................................................................. 50 </p><p>2.5.1 Network Survivability and Reliability ................................................ 50 2.5.2 Multiobjective Elitist GA with Restricted Mating.............................. 52 </p><p>2.5.2.1 Problem Encoding.................................................................... 52 2.5.2.2 Crossover Operator .................................................................. 52 2.5.2.3 Mutation ................................................................................... 53 2.5.2.4 Overall Algorithm .................................................................... 53 </p><p>2.5.3 Computational Experiments................................................................ 55 2.6 Other Metaheuristic Techniques to Multiobjective Reliability </p><p>Optimization............................................................................................... 56 2.6.1 Ant Colony Optimization.................................................................... 56 2.6.2 Simulated Annealing........................................................................... 57 2.6.3 Conclusions......................................................................................... 57 </p><p>References ........................................................................................................ 58 </p><p>3 Genetic Algorithm Applications in Surveillance and Maintenance Optimization Sebastin Martorell, Sofa Carlos, Jos F. Villanueva, Ana Snchez ................... 63 </p><p>3.1 Introduction ................................................................................................ 63 3.2 Analysis of Published Research ................................................................. 67 3.3 Overview of Testing and Maintenance ...................................................... 71 </p><p>3.3.1 RAMS and the role of T&amp;M at NPP................................................... 71 3.3.2 Failure types and T&amp;M activities ....................................................... 7...</p></li></ul>