G. C. Onwubolu, B. V. Babu

New Optimization Techniques in Engineering

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Godfrey C. OnwuboluB. V. Babu

New Optimization Techniques in Engineering

123

Professor Godfrey C. OnwuboluChair of Department of EngineeringThe University of the South Pacifi cP.O. Box 1168SuvaFiji Islands E-mail: onwubolu_g@usp.ac.fj

Professor B. V. BabuAssistant Dean – Engineering Services Division (ESD) & Head – Chemical Engineering & Engineering Technology DepartmentsBirla Institute of Technology & Science (BITS)Pilani - 333 031 (Rajasthan)IndiaE-mail: bvbabu@bits-pilani.ac.in; bv_babu@hotmail. com

ISSN 1434-9922

Library of Congress Cataloging-in-Publication-Data

Onwubolu, Godfrey C.New optimization techniques in engineering / Godfrey C. Onwubolu, B. V. Babu.p. cm. -- (Studies in fuzziness and soft computing ; v. 141)Includes bibliographical references and index. 1. Engineering--Mathemaical models. 2. Mathematica optimization. I. Babu, B. V. II. Title. III. Series.TA342.059 2004620'0015'196--dc22

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifi cally the rights of translation, reprinting, reuse of illustrations, recitations, broadcasting, reproduction on microfi lm 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 for prosecution under the German Copyright Law.

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The use of general descriptive names, registered names trademarks, etc. in this publication does not imply, even in the absence of a specifi c statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

Cover design: E. Kirchner, Springer-Verlag, Heidelberg Printed on acid free paper 62/3020/M - 5 4 3 2 1 0

Softcover reprint of the hardcover 1st edition 2004Originally published by Springer-Verlag Berlin Heidelberg in 2004

ISBN 978-3-642-05767-0 ISBN 978-3-540-39930-8 (eBook) DOI 10.1007/978-3-540-39930-8

PREFACE

Presently, general-purpose optimization techniques such as Simulated Annealing,and Genetic Algorithms, have become standard optimization techniques. Theseoptimization techniques commence with a single solution and then find the bestfrom several moves made, and generally, past history is not carried forward intothe present. Many researchers agree that firstly, having a population of initial solu-tions increases the possibility of converging to an optimum solution, and secondly,updating the current information of the search strategy from previous history is anatural tendency. Accordingly, attempts have been made by researchers to restruc-ture these standard optimization techniques in order to achieve the two goals men-tioned.

To achieve these two goals, researchers have made concerted efforts in the lastone-decade in order to invent novel optimization techniques for solving real lifeproblems, which have the attributes of memory update and population-basedsearch solutions. This book describes these novel optimization techniques, whichin most cases outperform their counterpart standard optimization techniques inmany application areas. Despite these already promising results, these novel opti-mization techniques are still in their infancy and can most probably be improved.To date, researchers are still carrying out studies on sound theoretical basis andanalysis to determine why some of these novel optimization techniques convergeso well compared to their counterpart standard optimization techniques.

Interestingly, most books that have reported the applications and results ofthese novel optimization techniques have done so without sufficiently consideringpractical problems in the different engineering disciplines. This book, New Opti-mization Techniques in Engineering has three main objectives: (i) to discuss in theclearest way possible, these novel optimization techniques, (ii) to apply thesenovel optimization techniques in the conventional engineering disciplines, and (iii)to suggest and incorporate the improvements in these novel optimization tech-niques that are feasible as and when it is possible in the application areas chosen.

To achieve the first objective, Part I containing seven chapters have been writ-ten by the inventors of these novel optimization techniques or experts who havedone considerable work in the areas (Genetic Algorithm, Memetic Algorithm,Scatter Search, Ant Colony Optimization, Differential Evolution, Self-OrganizingMigrating Algorithm, Particle Swarm Optimization). Genetic Algorithm has beenincluded for completeness since it is the progenitor of Memetic Algorithm. Thecontributors for Genetic Algorithm and Particle Swarm Optimization have beenchosen, not as the inventors, but due to their expertise and contributions in theseareas of optimization. To achieve the second objective, Part II contains several

VI Preface

chapters in which researchers have applied these novel optimization techniques todifferent Engineering disciplines such as Chemical/Metallurgical Engineering,Civil/Environmental Engineering/Interdisciplinary, Electrical/Electronics Engi-neering, Manufacturing/Industrial Engineering, and Mechanical/Aeronautical En-gineering. Firstly, the Engineering background is sufficiently given concerning theproblem-domain, and then a novel optimization technique is applied. Conse-quently, Part II makes it easy for engineers and scientists to understand the linkbetween theory and application of a particular novel optimization technique. Toachieve the third objective, the possible improvements in these novel optimizationtechniques are identified, suggested and applied to some of the engineering prob-lems successfully. Part III discusses newer areas, which are considered as ex-tended frontiers.

The text serves as an instructional material for upper division undergraduates,entry-level graduate student, and a resource material for practicing engineers, re-search scientists, operations researchers, computer scientists, applied mathemati-cians, and management scientists. Those to purchase the book include upper divi-sion undergraduates or entry-level graduate students, academics, professionals andresearchers of disciplines listed above, and libraries.

Godfrey C Onwubolu and B V BabuJune 2003

CONTRIBUTORS

B. V. Babu, Assistant Dean-Engineering Services Division & Head-Chemical Engineering& Engineering Technology Departments, Birla Institute of Technology and Science(BITS), Pilani-333 031 (Rajasthan), India. E-mail: bvbabu@bits-pilani.ac.in (CHAP.1, 9, 10, 11 & 12)

Laxmidhar Behera, Assistant Professor, Department of Electrical Engineering, Indian In-stitute of Technology, Kanpur-208 016, India. E-mail: lbehera@iitk.ac.in (CHAP. 19)

Sergiy Butenko, Department of Industrial and Systems Engineering, 373 Weil Hall, Uni-versity of Florida, Gainesville, FL 32611. E-mail: butenko@ufl.edu (CHAP. 29)

Antonella Carbonaro, Department of Computer Science, University of Bologna via Sac-chi, 3, 47023 Cesena, Italy. Email: carbonar@csr.unibo.it (CHAP. 15)

Kai-Ying Chen, Assistant Professor, Department of Industrial Engineering, NationalTaipei University of Technology. E-mail: kychen@ntut.edu.tw (CHAP. 25 & 28)

Mu-Chen Chen, Professor, Department of Business Management, National Taipei Univer-sity of Technology, No. 1, Section 3, Chung-Hsiao E. Road, Taipei 106, Taiwan, ROC.Email: bmcchen@ntut.edu.tw / iemcchen@yahoo.com.tw (CHAP. 25 & 28)

Maurice Clerc, France Télécom Recherche & Développement, 90000, Belfort, France.Email: Maurice.Clerc@ WriteMe.com (CHAP. 8)

Carlos Cotta, Associate Professor, University of Málaga, Departamento de Lenguajes yCiencias de la Computación Complejo Tecnológico (Despacho 3.2.49), Campus deTeatinos 29071-Málaga. SPAIN. Email: ccottap@lcc.uma.es (CHAP. 3 & 27)

Kalyanmoy Deb, Professor, Department of Mechanical Engineering, Indian Institute ofTechnology, Kanpur, Pin 208 016, INDIA. E-mail: deb@iitk.ac.in (CHAP. 2)

Luca Maria Gambardella, Director, IDSIA, Istituto Dalle Molle di Studi sull'IntelligenzaArtificiale Galleria 2 6928 Manno-Lugano, Switzerland. Email: luca@idsia.ch (CHAP.5)

Fred Glover, MediaOne Chaired Professor of Systems Science, University of Colorado;Visiting Hearin Professor, University of Mississippi; Research Director of the HearinCenter for Enterprise Science; School of Business Administration University of Mis-sissippi University, MS 38677. Email: fglover@bus.olemiss.edu (CHAP. 4 &13)

Lee Tong Heng, Department of Electrical & Computer Engineering, National University ofSingapore, 4 Engineering Drive 3, Singapore-117 576, E-mail: eleleeth@nus.edu.sg(CHAP. 20)

Hanno Hildmann, University of Amsterdam, Department of Sciences, The Netherlands.Email: hanno@dfki.de (CHAP. 15)

Nguyen Van Hop, Sirindhorn International Institute of Technology, Thammasat Univer-sity, P.O.Box 22, Klong Luang, Pathumthani 12121, Thailand. E-mail: van-hop@siit.tu.ac.th (CHAP. 14)

D. Nagesh Kumar, Associate Professor, Civil Engineering Department, Indian Institute ofScience, Bangalore-560 012, India. E-mail: nagesh@civil.iisc.ernet.in (CHAP. 16)

VIII Contributors

Manuel Laguna, Leeds School of Business, University of Colorado,Boulder, CO 80309-0419, USA. Email: Manuel.Laguna@Colorado.edu (CHAP. 4 & 13)

Fabio de Luigi, Dept. Computer Science, University of Ferrara, Italy.Email: f.deluigi@unife.it (CHAP. 5)

Jouni Lampinen, Department of Information Technology, Laboratory of InformationProcessing, Lappeenranta University of Technology, P.O.Box 20, FIN-53851 Lap-peenranta, Finland. E-mail: Jouni.Lampinen@lut.fi (CHAP. 6 & 26)

Alexandre Linhares, Adjunct Professor, Brazilian School of Business and Public Admini-stration, FGV, Praia de Botafogo 190/426, Rio de Janeiro 22257-970.Email: linhares@fgv.br (CHAP. 18)

Vittorio Maniezzo, Professor, Department of Computer Science, University of Bologna viaSacchi, 3, 47023 Cesena, Italy. Email: maniezzo@csr.unibo.it (CHAP. 5 & 15)

Rafael Marti, Departamento de Estadistica e Investigación Operativa, Facultad deMatemáticas, Universidad de Valencia, C/ Dr. Moliner 50, 46100 Burjassot, Valencia,Spain. E-mail: Rafael.Marti@uv.es (CHAP. 4 & 13)

Alexandre de Sousa Mendes, Universidade Estadual de Campinas – UNICAMPFaculdade de Engenharia Elétrica e de Computação – FEEC Departamento de Engen-haria de Sistemas - DENSIS C.P. 6101 - CEP 13083-970, Campinas - SP - BrazilE-mail: asmendes@yahool.com (CHAP.3, 18 & 27)

Pablo Moscato, Senior Lecturer and Postgraduate Director for Computer Science, Schoolof Electrical Engineering and Computer Science, Faculty of Engineering and Built En-vironment, The University of Newcastle, Callaghan, 2308 New South Wales, Austra-lia. Email: moscato@cs.newcastle.edu.au (CHAP. 3, 18, & 27)

Godfrey C. Onwubolu, Professor and Chair of Engineering, Department of Engineering,The University of the South Pacific, PO Box 1168, Suva, FIJI. Email: on-wubolu_g@usp.ac.fj (CHAP. 1, 21, 22, 23, & 24)

Panos M. Pardalos, Professor and Co-Director, Center for Applied Optimization, Indus-trial and Systems Engineering Department, 303 Weil Hall, University of Florida, POBox 116595, Gainesville, FL 32611-6595, Email: pardalos@ufl.edu / par-dalos@cao.ise.ufl.edu (CHAP. 29)

Xiao Peng, Department of Electrical & Computer Engineering, National University of Sin-gapore, 4 Engineering Drive 3, Singapore-117 576, E-mail: engp0525@nus.edu.sg(CHAP. 20)

K. Srinivasa Raju, Assistant Professor, Civil Engineering Group, Birla Institute of Tech-nology and Science (BITS), Pilani-333 031, Rajasthan, India. Email: ksraju@bits-pilani.ac.in (CHAP. 16)

Anuraganand Sharma, Computer Section, Colonial, Suva, Fiji. Email:ANDS@Colonial.com.au (CHAP. 23)

Rainer Storn, Infineon AG, TR PRM AL, Balanstr. 73, D-81541 Muenchen, Germany /International Computer Science Institute 1947 Center Street, Suite 600, Berkeley, CA94704-1198. E-mail: rainer.storn@infineon.com / storn@icsi.berkeley.edu (CHAP. 6)

Mario T. Tabucanon, Asian Institute of Technology, P.O. Box 4, Klong Kluang, Pathum-thani 1210, Thailand. E-mail: mtt@ait.ac.th (CHAP. 14)

Prahlad Vadakkepat, Assistant Professor, Department of Electrical & Computer Engi-neering, National University of Singapore & General Secretary, Federation of Interna-tional Robot-soccer Association, Singapore, E-mail: prahlad@nus.edu.sg (CHAP. 20).

Ivan Zelinka, Tomas Bata University in Zlin, Faculty of Technology, Institut of Informa-tion Technologies, Mostni 5139, Zlin 760 01, Czech Republic. Email: ze-linka@ft.utb.cz (CHAP. 7, 17 & 26)

Acknowledgements

The process of producing a book is the outcome of the inputs of many people. We

are grateful to all the contributors for their ideas and cooperation. Working to-

gether with such a number of contributors from remotely located distances is both

challenging and interesting; there was such a speedy response from contributors,

that they deserve to be acknowledged. On the publishers’ side, we want to thank

Thomas Ditzinger, Heather King and other colleagues at Springer-Verlag, Heidel-

berg, Germany for their enthusiasm and editorial hard work to get this book out to

the readers.

BVB acknowledges the Director, Prof. S. Venkateswaran; Deputy Director

(Administration), Prof. K.E.Raman; Deputy Director (Academic), Prof.

L.K.Maheshwari; and Deputy Director (Off-Campus Programmes), Prof. V.S.Rao

of BITS-Pilani for continuous encouragement, support, and providing him with

the required infrastructure facilities in preparing this book. He thanks his col-

leagues and Ph.D. Students Mr. Rakesh Angira and Mr. Ashish Chaurasia for

helping him in making some of the figures and proof reading.

Producing a book is not without the support and patience of some close people

to the authors. In this regard, GCO thanks his wife, Ngozi and their children

Chioma, Chineye, Chukujindu, Chinwe and Chinedu for their supporting-role and

forbearance. In addition, the undergraduate and graduate students who worked un-

der my supervision are acknowledged for their hardwork. BVB thanks his wife

Shailaja for unconditional support and understanding, and his children Shruti and

Abhinav for understanding the importance and seriousness of this project contri-

bution by not complaining on reaching home late nights. BVB also thanks his

parents Shri Venkata Ramana and Mrs. Annapurna for making him what he is to-

day.

CONTENTS

Chapter 1: Introduction 1 Godfrey C Onwubolu and B V Babu 1.1 Optimization 1 1.2 Stochastic Optimization Technique 4 1.2.1 Local Search 4 1.2.2 Population-based Search 5 1.3 Framework for Well-Established Optimization Techniques 6 1.4 New & Novel Optimization Techniques 7 1.5 The Structure of the Book 9 1.6 Summary 10

References 11

Part I: New Optimization Techniques

Chapter 2: Introduction to Genetic Algorithms for Engineering Optimization Kalyanmoy Deb 13 2.1 Introduction 13 2.2 Classical Search and Optimization Techniques 14 2.3 Motivation from Nature 16 2.4 Genetic Algorithms 17 2.4.1 Working Principle 17 2.4.2 An Illustration 22 2.4.3 A Hand Calculation 27 2.4.4 Constraint Handling 31 2.5 Car Suspension Design Using Genetic Algorithms 34 2.5.1 Two-dimensional model 34 2.5.2 Three-dimensional model 37 2.6 Real-Parameter Genetic Algorithms 40 2.7 A Combined Genetic Algorithm 43 2.7.1 Gear Train Design 44 2.8 A Spring Design 45 2.9 Advanced Genetic Algorithms 47 2.10 Conclusions 48

References 49

XII Contents

Chapter 3: Memetic Algorithms 53 Pablo Moscato, Carlos Cotta and Alexandre Mendes 3.1 Introduction 53 3.2 The MA Search Template 54 3.3 Design of Effective MAs 60 3.4 Applications of MAs 65 3.4.1 NP-hard Combinatorial Optimization problems 66 3.4.2 Telecomunications and networking 66 3.4.3 Scheduling and Timetabling Problems 67 3.4.4 Machine Learning and Robotics 67 3.4.5 Engineering, Electronics and Electromagnetics 68 3.4.6 Problems involving optimization in molecules 68 3.4.7 Other Applications 69 3.5 Conclusions and Future Directions 69

References 72 Chapter 4: Scatter Search and Path Relinking: Foundations andAdvanced Designs 87Fred Glover, Manuel Laguna and Rafael Martí 4.1 Introduction 87 4.2 Foundations 89 4.2.1 Scatter Search 89 4.2.2 Path Relinking 91 4.3 Advanced Strategies 93 4.3.1 Scatter Search 93 4.3.2 Path Relinking 96

References 99 Chapter 5: Ant Colony Optimization 101 Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi 5.1 Introduction 101 5.2 Ant Colony Optimization 102 5.2.1 Ant System 103 5.2.2 Ant Colony System 105 5.2.3 ANTS 107 5.3 Significant problems 109 5.3.1 Sequential ordering problem 110 5.3.2 Vehicle routing problems 111 5.3.3 Quadratic Assignment Problem 113 5.3.4 Other problems 114 5.4 Convergence proofs 115 5.5 Conclusions 116

References 117

Contents XIII

Chapter 6: Differential Evolution 123 Jouni Lampinen and Rainer Storn 6.1 Introduction 123 6.2 Mixed integer-discrete-continuous non-linear programming 124 6.3 Differential Evolution 125 6.3.1 Initialization 127 6.3.2 Mutation and Crossover 128 6.3.3 Selection 130 6.3.4 DE dynamics 132 6.4 Constraint handling 138 6.4.1 Boundary constraints 138 6.4.2 Constraint functions 139 6.4 Handling integer and discrete variables 142 6.5.1 Methods 142 6.5.2 An Illustrative Example 143 6.6 Numerical examples 144 6.6.1 Example 1: Designing a gear train 146 6.6.2 Example 2: Designing a pressure vessel 149 6.6.3 Example 3: Designing a coil compression spring 153 6.7 DE’s Sensitivity to Its Control Variables 157 6.8 Conclusions 160

References 163 Chapter 7: SOMA - Self-Organizing Migrating Algorithm 167 Ivan Zelinka 7.1 Introduction 167 7.2 Function domain of SOMA 168 7.3 Population 169 7.4 Mutation 170 7.5 Crossover 171 7.6 Parameters and Terminology 172 7.7 Principles of SOMA 175 7.8 Variations of SOMA 179 7.9 SOMA dependence on control parameters 180 7.10 On the SOMA classification and some additional information 182 7.11 Constraint Handling 184 7.11.1 Boundary constraints 185 7.11.2 Constraint functions 186 7.11.3 Handling of Integer and Discrete Variables 187 7.12 Selected Applications and Open Projects 189 7.13 Gallery of test functions 192 7.14 SOMA on tested functions 200 7.15 Conclusion 212

References 215

XIV Contents

Chapter 8: Discrete Particle Swarm Optimization, illustrated by theTraveling Salesman Problem 219Maurice Clerc 8.1 Introduction 219 8.2 A few words about “classical” PSO 219 8.3 Discrete PSO 221 8.4 PSO elements for TSP 222 8.4.1 Positions and state space 222 8.4.2 Objective function 222 8.4.3 Velocity 223 8.4.4 Opposite of a velocity 223 8.4.5 Move (addition) “position plus velocity” 223 8.4.6 Subtraction “position minus position” 224 8.4.7 Addition “velocity plus velocity” 224 8.4.8 Multiplication “coefficient times velocity” 224 8.4.9 Distance between two positions 225 8.5 The algorithm “PSO for TSP”. Core and variations 225 8.5.1 Equations 225 8.5.2 NoHope tests 226 8.5.3 ReHope process 227 8.5.4 Adaptive ReHope Method (ARM) 228 8.5.5 Queens 228 8.5.6 Extra-best particle 228 8.5.7 Parallel and sequential versions 229 8.6 Examples and results 229 8.6.1 Parameters choice 229 8.6.2 A toy example as illustration 230 8.6.3 Some results, and discussion 235

Appendix 236 References 238

Part II: Applications of New Optimization Techniques in Engineering

Part II.1: Chemical/Metallurgical Engineering

Chapter 9: Applications in Heat Transfer 241 B V Babu 9.1 Introduction 241 9.2 Heat Transfer Parameters in Trickle Bed Reactor 244 9.2.1 Orthogonal collocation 247 9.2.2 Experimental setup and procedure 249 9.2.3 Results and discussions 251 9.2.4 Conclusions 258

Contents XV

9.3 Design of Shell-and-Tube Heat Exchanger 2599.3.1 The Optimal HED problem 2599.3.2 Problem Formulation 2629.3.3 Results & Discussions 2639.3.4 Conclusions 276

Nomenclature 277References 281

Chapter 10: Applications in Mass Transfer 287B V Babu10.1 Introduction 28710.2 Optimization of Liquid Extraction Process 28710.2.1 Process Model 29010.2.2 Objective function 29110.2.3 Inequality constraints 29110.2.4 Results & Discussion 29210.2.5 Conclusions 29410.3 Optimization of a Separation Train of Distillation Columns 29510.3.1 Problem formulation 29510.3.2 Results & Discussion 29810.3.3 Conclusions 30010.4 Optimization and Synthesis of Heat Integrated Distillation Column

Sequences 30010.4.1 Problem formulation 30110.4.2 Synthesis of Distillation system 30310.4.3 Results & Discussion 30510.4.4 Conclusions 308

References 309

Chapter 11: Applications in Fluid Mechanics 313B V Babu11.1 Introduction 31311.2 Gas Transmission Network 31411.2.1 Problem Formulation 31511.2.2 Results & Discussion 32011.3 Water Pumping System 32711.3.1 Differential Evolution Strategies 32711.3.2 Problem Formulation 33111.3.3 Results & Discussion 33211.4 Conclusions 334

References 336

Chapter 12: Applications in Reaction Engineering 341B V Babu12.1 Introduction 341

XVI Contents

12.2 Design of Auto-Thermal Ammonia Synthesis Reactor 34312.2.1 Ammonia Synthesis Reactor 34312.2.2 Problem Formulation 34512.2.3 Simulated Results & Discussion 34512.2.4 Optimization 35212.2.5 Conclusions 35612.3 Thermal Cracking Operation 35612.3.1 Thermal Cracking 35712.3.2 Problem Description 35712.3.3 Problem Reformulation 36012.3.4 Simulated Results and Discussion 36112.3.5 Conclusions 362

References 363

Part II.2: Civil/Environmental Engineering/ Interdisciplinary

Chapter 13: New Ideas and Applications of Scatter Search and PathRelinking 367Fred Glover, Manuel Laguna and Rafael Martí13.1 Introduction 36713.2 Scatter Search Applications 36813.2.1 Neural Network Training 36813.2.2 Multi-Objective Routing Problem 36913.2.3 OptQuest: A Commercial Implementation 37113.2.4 A Context-Independent Method for Permutation Problems 37313.2.5 Classical Vehicle Routing 37513.3 Path Relinking Applications 37813.3.1 Matrix Bandwidth Minimization 37813.3.2 Arc Crossing Minimization 379

References 382

Chapter 14: Improvement of Search Process in Genetic Algorithms:An Application of PCB Assembly Sequencing Problem 385Nguyen Van Hop and Mario T Tabucanon14.1 Introduction 38514.2 Guided Genetic Algorithm (GGA) 38814.2.1 Coding scheme 38914.2.2 Fitness function 39014.2.3 Genetic Operators 39014.2.4 Input parameters 39414.2.5 Guided Genetic Algorithm (GGA) 39514.3 The GGA for the PCB Assembly Sequencing Problem 39614.3.1 The PCB Sequencing Problem on Multiple Non-identical Parallel

Machines 39614.3.2 Related works 399

Contents XVII

14.3.3 The GGA Solution 40114.3.4 Experimental Results 40314.4 Concluding Remarks 407

References 408

Chapter 15: An ANTS Heuristic for the Long-Term Car PoolingProblem: ACO 411Vittorio Maniezzo, Antonella Carbonaro, Hanno Hildmann15.1 Introduction 41115.2 Problem Definition and Formulation 41315.2.1 The objective function 41415.2.2 A four-indices mathematical formulation 41615.2.3 A set partitioning formulation 41815.2.4 Reduction rules 41815.3 The ANTS metaheuristic 42015.3.1 Attractiveness 42115.3.2 Trail update 42115.4 ANTS approaches for the LCPP 42215.4.1 Attractiveness quantification 42215.4.2 Local optimization 42315.5 A DSS for the LCPP 42415.6 Computational results 42615.7 Conclusions 429

References 430

Chapter 16: Genetic Algorithms in Irrigation Planning: A Case Studyof Sri Ram Sagar Project, India 431K Srinivasa Raju and D Nagesh Kumar16.1 Introduction 43116.1.1 Working Principle of Genetic Algorithms 43216.1.2 Necessity of Mathematical Modeling in Irrigation Planning 43316.2 Literature Review 43316.3 Irrigation System and Mathematical Modeling 43416.3.1 Continuity equation 43616.3.2 Crop area restrictions 43616.3.3 Crop water diversions 43616.3.4 Canal capacity restrictions 43716.3.5 Live storage restrictions 43716.3.6 Crop diversification considerations 43716.4 Results and Discussion 43716.5 Conclusions 441

References 443

XVIII Contents

Chapter 17: Optimization of Helical Antenna Electromagnetic Pattern Field Ivan Zelinka 445 17.1 Introduction 445 17.2 Problem description 445 17.3 Simulations 448 17.4 Software support 451 17.5 Conclusion 452

References 453

Chapter 18: VLSI design: Gate Matrix Layout Problem 455 Pablo Moscato, Alexandre Mendes and Alexandre Linhares 18.1 Introduction 455 18.2 The gate matrix layout problem 456 18.3 The memetic algorithm 458 18.3.1 Population structure 458 18.3.2 Representation and crossover 459 18.3.3 Mutation 461 18.3.4 Local search 462 18.3.5 Selection for recombination 466 18.3.6 Offspring insertion 467 18.3.7 Pseudo-code of the MA 468 18.3.8 Migration policies 469 18.4 Computational experiments 471 18.5 Discussion 475

References 477

Chapter 19: Parametric Optimization of a Fuzzy Logic Controllerfor Nonlinear Dynamical Systems using Evolutionary Computation 479Laxmidhar Behera19.1 Introduction 480 19.2 Differential Evolution 482 19.3 Simple Genetic Algorithm with Search Space Smoothing 483 19.4 Simple Genetic Algorithm Vs Differential Evolution 485 19.5 pH Neutralization Process 486 19.6 Simulation 488 19.7 Experiments & Results 490 19.8 The Univariate Marginal Distribution Algorithm 493 19.9 Robot arm control 493 19.9.1 Control Architecture 493 19.9.2 Inverse Dynamics Model 494 19.9.3 Feedback fuzzy PD Controller 497 19.10 Conclusions 499

References 500

Part II.3: Electrical/Electronics Engineering

Chapter 20: DNA Coded GA: Rule Base Optimization of FLCfor Mobile Robot 503Prahlad Vadakkepat, Xiao Peng and Lee Tong Heng20.1 Introduction 50320.2 DNA Computing 50420.3 The Khepera Robot and Webots Software 50620.3.1 The Khepera Robot 50620.3.2 The Webots Software 50720.4 The Fuzzy logic controller 50820.5 DNA coded Genetic Algorithm for FLC 51020.6 Simulation Results 51220.7 Discussion 514

References 515

Part II.4: Manufacturing/Industrial Engineering

Chapter 21: TRIBES application to the flow shop scheduling problem 517Godfrey C Onwubolu21.1 Introduction 51721.2 Flow-shop scheduling problem (FSP) 51821.3 TRIBES approach 51921.3.1 Terminology and concepts 51921.3.2 Informers 52021.3.3 Hyper-spheres, and promising areas 52021.3.4 Adaptations 52521.3.5 Adaptive scheme 52721.3.6 Transformer 52721.3.7 Local search 52821.3.8 The transformer-local search scheme 52821.3.9 Illustrating Tribes 52921.4 The TRIBES Algorithm 53021.5 Experimental results 53321.5.1 Parameter setting 53321.5.2 Comparison with other heuristics 53421.6 Conclusion 534

References 536

Chapter 22: Optimizing CNC Drilling Machine Operations:Traveling Salesman Problem-Differential Evolution Approach 537Godfrey C Onwubolu22.1 Introduction 53722.2 Travelling Salesman Problem (TSP) 53922.3 TSP using Closest Insertion Algorithm 54022.4 TSP using Differential Evolution 54422.4.1 Differential Evolution Method 544

Contents XIX

XX Contents

22.4.2 Differential Evolution Method for TSP 55122.4.3 Parameter Setting 55422.4.4 An Example 55422.4.5 Experimentation 55522.5 TSP/Differential Evolution Application in CNC Drilling of PCB 55622.5.1 PCB Manufacturing 55722.5.2 Automated Drilling Location and Hit Sequencing using DE 56022.6 Summary 562

References 564

Chapter 23: Particle swarm optimization for the assignment of facilitiesto locations 567Godfrey C Onwubolu and Anuraganand Sharma23.1 Introduction 56723.2 Problem Formulation 56823.3 Application of the PSO to the QAP 56923.3.1 Explosion Control 57223.3.2 Particle Swarm Optimization Operators 57323.3.3 Particle Swarm Optimization Neighborhood 57623.3.4 Particle Swarm Optimization Improvement Strategies 57723.4 Experimentation 58023.4.1 Parameter settings 58023.4.2 Computational results 58023.5 Conclusion 581

References 582

Chapter 24: Differential Evolution for the Flow Shop Scheduling Problem585Godfrey C Onwubolu 24.1 Introduction 58524.2 Problem Formulation for the flow shop schedules 58724.3 Differential Evolution 58924.3.1 Constraint Handling 59224.3.2 Integer and Discrete Optimization by Differential Evolution

Algorithm 59424.4 Illustrative Example 60224.4.1 Mutation Scheme 60324.4.2 Selection 60624.5 Experimentation 60624.5.1 Parameter Setting 60724.6 Summary 609

References 610

Chapter 25: Evaluation of Form Errors to Large Measurement DataSets Using Scatter Search 613Mu-Chen Chen and Kai-Ying Chen 25.1 Introduction 613 25.2 Mathematical Models for Roundness 615 25.2.1 Roundness 615 25.2.2 The maximum inscribed circle 616 25.2.3 The minimum circumscribed circle 617 25.2.4 The minimum zone circle 617 25.3 Mathematical Models for Sphericity 618 25.3.1 Sphericity 618 25.3.2 Maximum inscribed sphere 618 25.3.3 Minimum circumscribed sphere 619 25.3.4 Minimum zone sphere 620 25.4 Scatter Search 620 25.4.1 Overview of scatter search 620 25.4.2 Scatter search template 622 25.4.3 The scatter search procedure 624 25.5 Computational Experience 625 25.5.1 Roundness measurement 625 25.5.2 Sphericity measurement 626 25.6 Summary 627 References 630 Chapter 26: Mechanical engineering problem optimization by SOMA 633 Ivan Zelinka and Jouni Lampinen 26.1 Mechanical engineering problem optimization by SOMA 633 26.1.1 Designing a gear train 634 26.1.2 Designing a pressure vessel 638 26.1.3 Designing a coil compression spring 644 26.2 Conclusion 650 References 652

Chapter 27: Scheduling and Production & Control: MA 655 Pablo Moscato, Alexandre Mendes and Carlos Cotta 27.1 Introduction 655 27.2 The single machine scheduling problem 656 27.2.1 The test instances 658 27.2.2 The memetic algorithm approach 660 27.2.3 The SMS computational results 662 27.3 The parallel machine scheduling problem 665 27.3.1 The test instances 667 27.3.2 The memetic algorithm approach 667 27.3.3 The PMS computational results 668 27.4 The flowshop scheduling problem 670

Contents XXI

Part II.5: Mechanical/Aeronautical Engineering

XXII Contents

27.4.1 The test instances 67227.4.2 The memetic algorithm approach 67327.4.3 The flowshop computational results 67427.5 Discussion 677

References 679

Chapter 28: Determination of Optimal Machining Conditions UsingScatter Search 681Mu-Chen Chen and Kai-Ying Chen28.1 Introduction 68128.2 Fundamentals of CNC Turning 68228.2.1 CNC turning machine axes 68328.2.2 CNC turning operations 68328.2.3 CNC turning conditions 68328.3 Literature Review 68528.3.1 Machining optimization for turning operations 68528.3.2 Review of machining optimization techniques 68628.4 Notations in Machining Model 68928.5 The Multi-Pass Turning Model 69128.5.1 The cost function 69128.5.2 Turning condition constraints 69428.6 Computational Experience 69628.7 Conclusions 698

References 700

Part III: Extended Frontiers

Chapter 29: Extended Frontiers in optimization techniques 703Sergiy Butenko and Panos M Pardalos29.1 Recent Progress in Optimization Techniques 70329.2 Heuristic Approaches 70629.2.1 Parallel Metaheuristics 70729.3 Emerging Application Areas of Optimization 70829.4 Concluding Remarks 709

References 710