Modeling and Optimization O ptimization in Science and Technologies Technologies
Xin-She Yang Xin-She Yang Gebrail Bekdaş Sinan Melih Nigdeli
Editors
Metaheuristics and Optimization in Civil Engineering
Modeling and Optimization in Science and Technologies Volume 7
Series editors
Srikanta Patnaik, SOA University, Orissa, India e-mail:
[email protected] Ishwar K. Sethi, Oakland University, Rochester, USA e-mail:
[email protected] Xiaolong Li, Indiana State University, Terre Haute, USA e-mail:
[email protected] Editorial Board
Li Cheng, The Hong Kong Polytechnic University, Hong Kong Jeng-Haur Horng, National Formosa University, Yulin, Taiwan Pedro U. Lima, Institute for Systems and Robotics, Lisbon, Portugal Mun-Kew Leong, Institute of Systems Science, National University of Singapore Muhammad Nur, Diponegoro University, Semarang, Indonesia Luca Oneto, University of Genoa, Italy Kay Chen Tan, National University of Singapore, Singapore Sarma Yadavalli, University of Pretoria, South Africa Yeon-Mo Yang, Kumoh National Institute of Technology, Gumi, South Korea Liangchi Zhang, The University of New South Wales, Australia Baojiang Zhong, Soochow University, Suzhou, China Ahmed Zobaa, Brunel University, Uxbridge, Middlesex, UK
Modeling and Optimization in Science and Technologies Volume 7
Series editors
Srikanta Patnaik, SOA University, Orissa, India e-mail:
[email protected] Ishwar K. Sethi, Oakland University, Rochester, USA e-mail:
[email protected] Xiaolong Li, Indiana State University, Terre Haute, USA e-mail:
[email protected] Editorial Board
Li Cheng, The Hong Kong Polytechnic University, Hong Kong Jeng-Haur Horng, National Formosa University, Yulin, Taiwan Pedro U. Lima, Institute for Systems and Robotics, Lisbon, Portugal Mun-Kew Leong, Institute of Systems Science, National University of Singapore Muhammad Nur, Diponegoro University, Semarang, Indonesia Luca Oneto, University of Genoa, Italy Kay Chen Tan, National University of Singapore, Singapore Sarma Yadavalli, University of Pretoria, South Africa Yeon-Mo Yang, Kumoh National Institute of Technology, Gumi, South Korea Liangchi Zhang, The University of New South Wales, Australia Baojiang Zhong, Soochow University, Suzhou, China Ahmed Zobaa, Brunel University, Uxbridge, Middlesex, UK
About this Series
The book series Modeling and Optimization in Science and Technologies (MOST) publishes basic principles as well as novel theories and methods in the fast-evolving �eld of modeling and optimization. Topics of interest include, but are not limited to: meth methods ods for for anal analys ysis is,, desi design gn and and cont control rol of comp comple lex x syst system ems, s, netw networ orks ks and and machines; methods for analysis, visualization and management of large data sets; use of supercomputers for modeling complex systems; digital signal processing; molecular modeling; and tools and software solutions for different scienti �c and technological purposes. Special emphasis is given to publications discussing novel theories and practical solutions that, by overcoming the limitations of traditional methods, may successfully address modern scienti �c challenges, thus promoting scienti�c and technological progress. The series publishes monographs, contributed volumes and conference proceedings, as well as advanced textbooks. The main targets of the series are graduate students, researchers and professionals working at the forefront of their �elds.
More information about this series at http://www.springer.com/series/10577 at http://www.springer.com/series/10577
Xin-She Yang Gebrail Bekdaş Sinan Melih Nigdeli •
Editors
Metaheuristics and Optimization in Civil Engineering
1 3
Editors Xin-She Yang School of Science and Technology Middlesex University London UK
Sinan Melih Nigdeli Faculty of Engineering Istanbul University Istanbul Turkey
Gebrail Bekdaş Faculty of Engineering Istanbul University Istanbul Turkey
ISSN 2196-7326 ISSN 2196-7334 (electronic) Modeling and Optimization in Science and Technologies ISB ISBN 978978-33-31 3199-26 2624 2433-7 7 ISBN SBN 978978-33-31 3199-26 2624 2455-1 1 (eB (eBook) ook) DOI 10.1007/978-3-319-26245-1 Library of Congress Control Number: 2015954625 Springer Cham Heidelberg New York Dordrecht London © 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 materi material al is concer concerned ned,, speci speci�cally cally the rights rights of transl translati ation, on, reprint reprinting ing,, reuse reuse of illustr illustrati ations ons,, recitation, recitation, broadcastin broadcasting, g, reproduction reproduction on micro�lms or in any other physic physical al way, way, and transmis transmissio sion n or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use use of gene genera rall desc descri ript ptiv ivee name names, s, regis registe tere red d name names, s, trad tradem emar arks ks,, serv servic icee mark marks, s, etc. etc. in this this publication publication does not imply, even in the absence absence of a speci �c statement, statement, that such names are exempt 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 book are believed believed to be true true and accurate accurate at the date of public publicati ation. on. Neither Neither the publis publishe herr 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 Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com)
Preface
Almo Almost st all all desi design gn probl problem emss in engi engine neer erin ing g can can be cons consid ider ered ed as opti optimi mizat zatio ion n problem problemss and thus thus require require optimi optimizat zation ion techniq techniques ues to solve. solve. Howeve However, r, as most most real-world problems are highly nonlinear, traditional optimization methods usually do not work well. The current trend is to use evolutionary algorithms and metaheuris heuristic tic optimi optimizat zation ion method methodss to tackle tackle such such nonline nonlinear ar optimi optimizat zation ion problem problems. s. Meta Metahe heuri urist stic ic algo algori rith thms ms have have gain gained ed huge huge popul populari arity ty in rece recent nt year years. s. Thes Thesee metaheuristic algorithms include genetic algorithms, particle swarm optimization, bat algori algorithm thm,, cuckoo cuckoo search, search, differ differenti ential al evolut evolution ion,, �refly algori algorithm thm,, harmony harmony search, flower pollination algorithm, ant colony optimization, bee algorithms, and many many others others.. The popular popularity ity of naturenature-ins inspir pired ed metahe metaheuri uristi sticc algori algorithm thmss can be attributed to their good characteristics because these algorithms are simple, flexible, ef �cient, and adaptable, and yet easy to implement. Such advantages make them versatile to deal with a wide range of optimization problems without much a priori knowledge about the problem to be solved. Meta Metahe heur uris isti ticc algor algorit ithm hmss play play an impor importa tant nt role role in the the opti optimu mum m desi design gn of complex engineering problems when analytical approaches and traditional methods are are not not effe effect ctive ive for for solv solvin ing g nonli nonline near ar desi design gn probl problem emss in civi civill engi engine neer erin ing. g. Genera Generally lly speaki speaking, ng, these these design design problem problemss are highly highly nonlin nonlinear ear with with comple complex x constraints, and thus are also highly multimodal. These design constraints often come from design requirements and security measures such as the stresses on the members due to external loading, environmental factors, and usability under service loads. A mathematical solution may be the best approach in an ideal world, but in engineering designs, the values of a design variable such as mass or length must be realis realistic; tic; for exampl example, e, quanti quantitie tiess must must be nonneg nonnegati ative. ve. In additi addition, on, such such design design values must correspond to something that can be manufacturable in practice. For all engineering disciplines, optimization is crucially important in the design process so as to �nd a good balance between economy and security that are the prim primar ary y goal goalss of desi designs gns.. Aest Aesthe heti tics cs and and prac practi tica cabil bilit ity y are are also also impor importa tant nt in real-world applications. Civil engineering is probably the oldest engineering discipl ciplin inee and and it has has alwa always ys been been link linked ed to the the cons constr truc ucti tion on and and real realiz izat atio ion n of
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Preface
civilization. In fact, optimization may be more relevant in civil engineering than in other engineering disciplines. For example, in designing a non-critical machine part in mechanical engineering, the stresses on the part must not exceed certain limits. If a stronger part is used, it may become too expensive. On the other hand, a weaker part may still be able to make the machine work properly, but in time such weak parts can be worn off or damaged. However, such parts may be easy to be replaced at low costs. If this is the case, machine serviceability can be maintained in practice. But in civil engineering, structural integrity and safety may impose stringent restrictions on the structural members that may not be easily replaced. In such cases, all design constraints and the best possible balance between security and economy must be found without risking lives. In addition, sometimes, the minor improvement may not be as important as robustness in applications. A robust design should be able to handle uncertainties in terms of material properties, manufacturing tolerance, and load irregularity in service. Due to complexity and a large number of design constraints in civil engineering, traditional methods often struggle to cope with such high nonlinearity and multimodality. Thus, metaheuristic optimization methods have become important tools in the optimum design in civil engineering. This edited book strives to summarize the latest developments in optimization and metaheuristic algorithms with emphasis on applications in civil engineering. Topics include the overview of meteaheuristic algorithms and optimization, structural optimization by flower pollination algorithm, steel design by swarm intelligence, optimum seismic design of steel frames by bat algorithm, 3D truss optimization by genetic algorithms, reactive power optimization by cuckoo search, structural design by harmony search, asphalt pavement management, reinforced concrete beam design, transport infrastructure planning, water distribution networks, capacitated vehicle routing, slope stability problems, and others. Therefore, this timely book can serve as an ideal reference for graduates, lecturers, engineers, and researchers in civil engineering, mechanical engineering, transport and geotechnical engineering. It can also serve as a timely reference for relevant university courses in all disciplines in civil engineering. We would like to thank the editors and staff at Springer for their help and professionalism. Last but not least, we thank our families for their help and support. June 2015
Xin-She Yang Gebrail Bekdaş Sinan Melih Nigdeli
Contents
Review and Applications of Metaheuristic Algorithms in Civil Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Xin-She Yang, Gebrail Bekdaş and Sinan Melih Nigdeli
1
Application of the Flower Pollination Algorithm in Structural Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sinan Melih Nigdeli, Gebrail Bekda ş and Xin-She Yang
25
Use of Swarm Intelligence in Structural Steel Design Optimization . . . . Mehmet Polat Saka, Serdar Carbas, Ibrahim Aydogdu and Alper Akin
43
Metaheuristic Optimization in Structural Engineering . . . . . . . . . . . . . S.O. Degertekin and Zong Woo Geem
75
Performance-Based Optimum Seismic Design of Steel Dual Braced Frames by Bat Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . Saeed Gholizadeh and Hamed Poorhoseini
95
Genetic Algorithms for Optimization of 3D Truss Structures . . . . . . . . 115 Vedat Toğan and Ayşe Turhan Daloğlu Hybrid Meta-heuristic Application in the Asphalt Pavement Management System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fereidoon Moghadas Nejad, Ashkan Allahyari Nik and H. Zakeri
135
Optimum Reinforced Concrete Design by Harmony Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gebrail Bekdaş, Sinan Melih Nigdeli and Xin-She Yang
165
Reactive Power Optimization in Wind Power Plants Using Cuckoo Search Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . K.S. Pandya, J.K. Pandya, S.K. Joshi and H.K. Mewada
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