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Modern Optimization Methods forScience, Engineering and

Technology

Modern Optimization Methods forScience, Engineering and

Technology

Edited byG R Sinha

Myanmar Institute of Information Technology Mandalay, Myanmar

IOP Publishing, Bristol, UK

ª IOP Publishing Ltd 2020

All rights reserved. No part of this publication may be reproduced, stored in a retrieval systemor transmitted in any form or by any means, electronic, mechanical, photocopying, recordingor otherwise, without the prior permission of the publisher, or as expressly permitted by law orunder terms agreed with the appropriate rights organization. Multiple copying is permitted inaccordance with the terms of licences issued by the Copyright Licensing Agency, the CopyrightClearance Centre and other reproduction rights organizations.

Permission to make use of IOP Publishing content other than as set out above may be soughtat [email protected].

G R Sinha has asserted his right to be identified as the author of this work in accordance withsections 77 and 78 of the Copyright, Designs and Patents Act 1988.

ISBN 978-0-7503-2404-5 (ebook)ISBN 978-0-7503-2402-1 (print)ISBN 978-0-7503-2403-8 (mobi)

DOI 10.1088/978-0-7503-2404-5

Version: 20191101

IOP ebooks

British Library Cataloguing-in-Publication Data: A catalogue record for this book is availablefrom the British Library.

Published by IOP Publishing, wholly owned by The Institute of Physics, London

IOP Publishing, Temple Circus, Temple Way, Bristol, BS1 6HG, UK

US Office: IOP Publishing, Inc., 190 North Independence Mall West, Suite 601, Philadelphia,PA 19106, USA

Dedicated to my late grandparents, my teachers and Revered Swami Vivekananda.

Contents

Preface xvi

Acknowledgements xvii

Editor biography xviii

List of contributors xx

1 Introduction and background to optimization theory 1-1

1.1 Historical development 1-1

1.1.1 Robustness and optimization 1-2

1.2 Definition and elements of optimization 1-3

1.2.1 Design variables and parameters 1-4

1.2.2 Objectives 1-4

1.2.3 Constraints and bounds 1-5

1.3 Optimization problems and methods 1-6

1.3.1 Workflow of optimization methods 1-6

1.3.2 Classification of optimization methods 1-8

1.4 Design and structural optimization methods 1-9

1.4.1 Structural optimization 1-9

1.4.2 Design optimization 1-11

1.5 Optimization for signal processing and control applications 1-11

1.5.1 Signal processing optimization 1-12

1.5.2 Communication and control optimization 1-13

1.6 Design vectors, matrices, vector spaces, geometry and transforms 1-13

1.6.1 Linear algebra, matrices and design vectors 1-14

1.6.2 Vector spaces 1-15

1.6.3 Geometry, transforms, binary and fuzzy logic 1-15

References 1-17

2 Linear programming 2-1

2.1 Introduction 2-1

2.2 Applicability of LPP 2-3

2.2.1 The product mix problem 2-3

2.2.2 Diet problem 2-4

2.2.3 Transportation problem 2-4

vii

2.2.4 Portfolio optimization 2-8

2.3 The simplex method 2-10

2.4 Artificial variable techniques 2-12

2.5 Duality 2-14

2.6 Sensitivity analysis 2-15

2.7 Network models 2-17

2.7.1 Shortest path problem 2-17

2.8 Dual simplex method 2-18

2.9 Software packages to solve LPP 2-19

Further reading 2-19

3 Multivariable optimization methods for risk assessment of thebusiness processes of manufacturing enterprises

3-1


3.2 A mathematical model of a business process 3-5

3.3 The market and specific risks, the features of their account 3-6

3.4 Measurement of the risk of using the discount rate, expertassessments and indicators of sensitivity

3-12

3.5 Conclusion 3-24

References 3-24

4 Nonlinear optimization methods—overview and future scope 4-1


4.1.1 Optimization 4-2

4.1.2 NLP 4-4

4.1.3 Nonlinear optimization problem and models 4-5

4.2 Convex analysis 4-6

4.2.1 Sets and functions 4-6

4.2.2 Convex cone 4-7

4.2.3 Concave function 4-7

4.2.4 Nonlinear optimization: the interior-point approach 4-7

4.3 Applications of nonlinear optimizations techniques 4-10

4.3.1 LOQO: an interior-point code for NLP 4-10

4.3.2 Digital audio filter 4-10

4.4 Future research scope 4-11

References 4-11

Modern Optimization Methods for Science, Engineering and Technology

viii

5 Implementing the traveling salesman problem usinga modified ant colony optimization algorithm

5-1

5.1 ACO and candidate list 5-1

5.2 Description of candidate lists 5-2

5.3 Reasons for the tuning parameter 5-3

5.4 The improved ACO algorithm 5-3

5.4.1 Dynamic candidate set based on nearest neighbors 5-6

5.4.2 Heuristic parameter updating 5-8

5.5 Improvement strategy 5-10

5.5.1 2-Opt local search 5-10

5.6 Procedure of IACO 5-11

5.7 Flow of IACO 5-12

5.8 IACO for solving the TSP 5-12

5.9 Implementing the IACO algorithm 5-15

5.10 Experiment and performance evaluation 5-19

5.10.1 Evaluation criteria 5-20

5.10.2 Path evaluation model 5-20

5.10.3 Evaluation of solution quality 5-21

5.11 TSPLIB and experimental results 5-21

5.11.1 Experiment 1 (analysis of tour length results) 5-22

5.11.2 Experiment 2 (comparison of convergence speed) 5-25

5.12 Comparison experiment 5-27

5.13 Analysis on varying number of ants 5-34

5.13.1Analysis of ants starting at different cities versus the same city 5-34

5.13.2Analysis on an increasing number of ants versus number ofiterations

5-36

5.14 IACO comparison results 5-40

5.15 Conclusions 5-41

References 5-42

6 Application of a particle swarm optimization techniquein a motor imagery classification problem

6-1


6.1.1 Literature review 6-4

6.1.2 Motivation and requirements 6-6

6.2 Particle swarm optimization 6-7

6.2.1 The mathematical model of PSO 6-8


ix

6.2.2 Constraint-based optimization 6-10

6.3 Proposed method 6-11

6.3.1 Materials and methods 6-12

6.3.2 Classification 6-14

6.4 Results 6-20

6.5 Conclusion 6-22

References 6-23

7 Multi-criterion and topology optimization using Lie symmetriesfor differential equations

7-1


7.2 Fundamentals of topological manifolds 7-3

7.2.1 Analytic manifolds 7-3

7.2.2 Lie groups and vector fields 7-4

7.3 Differential equations, groups and the jet space 7-7

7.3.1 Prolongation of group action and vector fields 7-8

7.3.2 Total derivatives of vector fields and generalprolongation formula

7-8

7.3.3 Criterion of maximal rank and infinitesimalinvariance for differential equations

7-11

7.3.4 Differential equations and symmetry groups 7-11

7.3.5 Differential invariants and the group invariant solutions 7-13

7.4 Classification of the group invariant solutions and optimal solutions 7-14

7.4.1 Adjoint representation for the cKdV and optimizationof the group generators

7-14

7.4.2 Calculation of the optimal group invariant solutionsfor the cKdV

7-18

7.5 Concluding remarks 7-20

References 7-20

8 Learning classifier system 8-1


8.2 Background 8-2

8.3 Classification learner tools 8-3

8.3.1 MATLAB®: classification learner app 8-3

8.3.2 BigML® 8-4

8.3.3 Microsoft® AzureML® 8-4


x

8.4 Sample dataset 8-4

8.4.1 Splitting the dataset 8-5

8.5 Learning classifier algorithms 8-6

8.5.1 Logistic regression classifiers 8-8

8.5.2 Decision tree classifiers 8-12

8.5.3 Discriminant analysis classifiers 8-15

8.5.4 Support vector machine classifiers 8-16

8.5.5 Nearest neighbor classifiers 8-17

8.5.6 Ensemble classifiers 8-18

8.6 Performance 8-18

8.6.1 Confusion matrix 8-20

8.6.2 Receiver operating characteristic 8-25

8.6.3 Parallel plot 8-27

8.7 Conclusion 8-28

Acknowledgments 8-29

References 8-29

9 A case study on the implementation of six sigmatools for process improvement

9-1


9.1.1 Generation and cleaning of BF gas 9-2

9.2 Problem overview 9-3

9.3 Project phase summaries 9-4

9.3.1 Definition 9-4

9.3.2 Measurement 9-5

9.3.3 Analyze and improvement 9-15

9.3.4 Control 9-20

9.4 Conclusion 9-20

9.4.1 Financial benefits 9-20

9.4.2 Non-financial benefits 9-20

10 Performance evaluations and measures 10-1

10.1 Performance measurement models 10-1

10.1.1 Fuzzy sets 10-2

10.2 AHP and fuzzy AHP 10-3

10.2.1 Fuzzy AHP 10-4

10.2.2 Linear programming method 10-4


xi

10.3 Performance measurement in the production approach 10-5

10.3.1 Free disposability hull 10-6

10.4 Data envelopment analysis 10-6

10.4.1 CCR model 10-7

10.4.2 BCC model 10-13

10.4.3 Other models 10-14

10.5 R as a tool for DEA 10-16

References 10-17

11 Evolutionary techniques in the design of PID controllers 11-1

11.1 The PID controller 11-2

11.1.1 Design procedure 11-3

11.1.2 Method 1: PID controller design using PSO 11-5

11.1.3 Method 2: PID controller design using BBBC 11-13

11.2 FOPID controller 11-17

11.2.1 Statement of the problem 11-18

11.2.2 BBBC aided tuning of FOPID controller parameters 11-18

11.2.3 Illustrative examples 11-18

11.3 Conclusion 11-22

References 11-26

12 A variational approach to substantial efficiency forlinear multi-objective optimization problems withimplications for market problems

12-1


12.2 Background 12-5

12.3 A review of substantial efficiency 12-8

12.4 New results and examples 12-9

12.5 Conclusion 12-24

References 12-25

13 A machine learning approach for engineeringoptimization tasks

13-1

13.1 Optimization: classification hierarchy 13-2

13.2 Optimization problems in machine learning 13-5


xii

13.3 Optimization in supervised learning 13-6

13.3.1 Bayesian optimization 13-7

13.3.2 Bayesian optimization for weight computation: a case study 13-8

13.3.3 Bayesian optimal classification: a case study 13-9

13.3.4 Bayesian optimization via binary classification: a case study 13-16

13.4 Optimization for feature selection 13-18

13.4.1 Feature extraction using precedence relations: a case study 13-20

13.4.2 Feature extraction via ensemble pruning: a case study 13-23

13.4.3 Feature-vector ranking metrics 13-25

References 13-26

14 Simulation of the formation process of spatial finestructures in environmental safety managementsystems and optimization of the parameters ofdispersive devices

14-1

14.1 The use of spatial finely dispersed multiphase structuresin ensuring ecological and technogenic safety

14-2

14.1.1 Analysis of recent research and publications 14-2

14.1.2 Statement of the problem and its solution 14-4

14.2 Physical and mathematical simulation of the creation processof spatial finely dispersed structures

14-5

14.2.1 Gas phase study and mathematical model description 14-5

14.2.2 Dispersed phase study and mathematical model description 14-8

14.2.3 Mathematical model of interfacial interaction 14-10

14.3 Numerical simulation of the formation of spatial dispersedstructures and the determination of the most effective waysof supplying fluid to eliminate various hazards

14-11

14.3.1 Ensuring numerical solution stability, convergenceand accuracy

14-11

14.3.2 Description of the numerical integration methodof the dispersed phase equations

14-12

14.3.3 Results of numerical simulation of a spatial finely dispersedstructure creation process which suppresses dust

14-14

14.3.4 Results of numerical simulation of the spatial finelydispersed structure creation process, which instantlyreduces the gas stream temperature

14-24

14.4 General conclusions 14-36

References 14-36


xiii

15 Future directions: IoT, robotics and AI based applications 15-1


15.1.1 The impact of AI and robotics in medicine and healthcare 15-3

15.1.2 Advances in AI technology and their impact onthe workforce

15-4

15.1.3 AI technologies and human intelligence 15-6

15.2 Cloud robotics, remote brains and their implications 15-7

15.2.1 Cloud computing and the RoboEarth project 15-9

15.2.2 The DAvinCi platform as a service (PaaS) surgical robot 15-9

15.3 AI and innovations in industry 15-10

15.3.1 Watson Analytics and data science 15-11

15.4 Innovative solutions for a smart society using AI, robotics andthe IoT

15-11

15.4.1 Cyber-physical systems (CPSs) 15-12

15.4.2 IoT architecture, its enabling technologies, securityand privacy, and applications

15-14

15.4.3 The Internet of robotic things (IoRT) and Industry 4.0 15-16

15.4.4 Cloud robotics and Industry 4.0 15-17

15.4.5 Opportunities, challenges and future directions 15-18

15.5 The human 4.0 or the Internet of skills (IoS) and the tactileInternet (zero delay Internet)

15-20

15.6 Future directions in robotics, AI and the IoT 15-20

References 15-23

16 Efficacy of genetic algorithms for computationally intractableproblems

16-1


16.2 Genetic algorithm implementation 16-3

16.3 Convergence analysis of the genetic algorithm 16-12

16.4 Key factors 16-14

16.4.1 Exploitation and exploration 16-14

16.4.2 Constrained optimization 16-15

16.4.3 Multimodal optimization 16-16

16.4.4 Multi-objective optimization 16-17

16.5 Concluding remarks 16-18

References 16-18


xiv

17 A novel approach for QoS optimization in4G cellular networks

17-1

17.1 Mobile generations 17-1

17.2 OFDMA networks 17-2

17.2.1 Limitations of FDMA, TDMA and WCDMA networks 17-3

17.2.2 Features of OFDMA networks 17-3

17.2.3 Quality of service in OFDMA networks 17-5

17.2.4 QoS improvement techniques in OFDMA networks 17-6

17.3 Simulation model and parameters 17-9

17.3.1 Simulation topology 17-9

17.3.2 Performance metrics 17-10

17.4 Adaptive rate scheduling in OFDMA networks 17-10

17.4.1 Introduction 17-10

17.4.2 Adaptive rate scheduling algorithm 17-11

17.4.3 Average scheduling delay estimation for the ARS scheme 17-13

17.5 Conclusions 17-13

References 17-13


xv

Preface

Optimization is generally defined as the process by which an optimum solution of aproblem is achieved. Optimization methods are designed to provide the best possiblesolution or values in engineering problems or system design. These methods areintended to improve the performance of a system in terms of several performanceevaluation factors, such as cost, time, computational complexity, raw materials, etc.Genetic algorithms are one of the most popular areas of study and are based on anoptimization theory which works by utilizing the concept of evolution and naturalselection. Achieving better solutions and improving the performance of existingsystem designs is an ongoing and continuous process on which scientists, engineers,mathematicians, philosophers and researchers have been working for many years.Optimization techniques are widely used in a wide range of applications, such asrobotics, artificial intelligence (AI) based applications, the chemical, electrical andmanufacturing industries, and many others.

This book focuses on the following: an introduction and background; linearprogramming; multivariable methods for risk assessment; an overview of nonlinearmethods; implementation of the traveling salesman problem using modified antcolony optimization; the application of particle swarm optimization; multi-criterionand topology optimization; learning classifiers; case studies on six sigma real-timesteel industry applications; performance measures and evaluation; multi-objectiveoptimization problems; machine learning approaches; genetic algorithms and theirapplication; QoS optimization in cellular networks; and the future directions ofoptimization methods and applications.

The purpose of this book is to present the fundamentals, background andtheoretical concepts of optimization principles in a comprehensive manner, alongwith potential applications and implementation strategies. This book covers casestudies, real-time applications, development objectives and research directions, inaddition to the basic fundamentals. The book will be very useful for a wide spectrumof readers, such as research scholars, academics and industry professionals, inparticular for those who are working on solving optimization issues, challenges andproblems.

xvi

Acknowledgements

I begin by expressing my sincere thanks to my wife Shubhra, my daughter Sampratiand my wonderful parents for their great support and encouragement throughoutthe completion of this important book. This book is the outcome of focused andsincere efforts that could be given to the book only due to the great support of myfamily.

I am grateful to my teachers who have left no stone unturned in empowering andenlightening me, in particular Shri Bhagwati Prasad Verma who is like a godfatherto me. I extend my heartfelt thanks to the Ramakrishna Mission order and ReveredSwami Satyaroopananda of Ramakrishna Mission, Raipur, India.

I extend my sincere thanks to all the contributors for writing on the relevanttheoretical background and real-time applications of optimization methods andentrusting upon me the role of editor.

I also wish to thank all my friends, well-wishers and all those who keep memotivated to do more and more, better and better (as is the objective of anyoptimization method).

My reverence with folded hands to Swami Vivekananda who has been the sourceof inspiration for all my work and achievements.

Last, but most importantly, I express my humble thanks to Dr John Navas,Senior Commissioning Manager of IOP Publishing for his great support, necessaryhelp, appreciation and quick responses. It has been wonderful experience workingwith John. My Thanks to Daniel Heatley of IOP for the necessary support and manyothers in the IOP team who helped me directly or indirectly. I also wish to thank IOPPublishing for giving me this opportunity to contribute on a relevant topic with areputed publisher.

xvii

Editor biography

G R Sinha

Dr G R Sinha is Adjunct Professor at the Institute of InformationTechnology (IIIT) Bangalore and is currently deputed as a Professorat Myanmar Institute of Information Technology (MIIT),Mandalay, Myanmar. He obtained his BE (electronics engineering)and MTech (computer technology) with a Gold Medal from theNational Institute of Technology, Raipur, India. He received hisPhD in electronics and telecommunications engineering from

Chhattisgarh Swami Vivekanand Technical University (CSVTU), Bhilai, India.He has published 227 research papers in various international and national

journals and conferences. He is an active reviewer and editorial member of morethan 12 reputed international journals such IEEE’s Transactions on ImageProcessing, Elsevier’s Computer Methods and Programs in Biomedicine, etc. Hehas been Dean of Faculty and Executive Council Member of CSVTU India and iscurrently a member of the Senate of MIIT. Dr Sinha has been appointed as an ACMDistinguished Speaker in the field of DSP for the years 2017–20. He has also beenappointed as an Expert Member for the Vocational Training Program by TataInstitute of Social Sciences (TISS) for two years (2017–19). He has been theChhattisgarh Representative of the IEEE MP Sub-Section Executive Council forthe last three years. He has served as a Distinguished Speaker in Digital ImageProcessing for the Computer Society of India (2015). He also served asDistinguished IEEE Lecturer on the IEEE India council for the Bombay section.He has been a Senior Member of IEEE for many years.

He is the recipient of many awards, such as the TCS Award 2014 for OutstandingContributions in the Campus Commune of TCS, R B Patil ISTE National Award2013 for Promising Teacher by ISTE New Delhi, Emerging Chhattisgarh Award2013, Engineer of the Year Award 2011, Young Engineer Award 2008, YoungScientist Award 2005, IEI Expert Engineer Award 2007, ISCA Young ScientistAward 2006, and the nomination and awarding of the Deshbandhu MeritScholarship for five years. He has authored six books, including Biometricspublished by Wiley India, a subsidiary of John Wiley, and Medical ImageProcessing, published by Prentice Hall of India. He is a consultant for various skilldevelopment initiatives of NSDC, Government of India. He is a regular referee ofproject grants under the DST-EMR scheme and several other schemes of theGovernment of India. He has delivered many keynote/invited talks and chairedmany technical sessions at international conferences in Singapore, Myanmar,Bangalore, Mumbai, Trivandrum, Hyderabad, Mysore, Allahabad, Nagercoil,Nagpur, Kolaghat, Yangon, Meikhtila and many other places. His special sessionon ‘Deep Learning in Biometrics’ was included in the IEEE InternationalConference on Image Processing in 2017. He is a Fellow of IETE New Delhi anda member of international professional societies such as IEEE, ACM and many

xviii

other national professional bodies such as ISTE, CSI, ISCA and IEI. He is amember of various committees of the university and has been Vice President of theComputer Society of India for the Bhilai chapter for two consecutive years. He hassupervised eight PhD scholars and 15 MTech scholars. His research interests includeimage processing and computer vision, optimization methods, employability skills,outcome based education (OBE), etc.


xix

List of contributors

Sirajuddin AhmedJamia Millia IslamiaNew DelhiIndia

Rajesh ChamorshikarBhilai Steel PlantBhilaiChhattisgarhIndia

Siddharth ChoubeySSTC-SSGICSVTUBhilaiChhattisgarhIndia

Abha ChoubeySSTC-SSGICSVTUBhilaiChhattisgarhIndia

Sien DengDepartment of Mathematical SciencesNorthern Illinois UniversityDeKalb, ILUSA

Santosh R DesaiElectronics and Instrumentation EngineeringBMS College of EngineeringBasavangudiBangaloreIndia

Somesh Kumar DewanganSSTC-SSGICSVTUBhilaiChhattisgarhIndia

xx

Vladimir GorbunovMoscow Institute of Electronic TechnologyMoscowRussia

Shankru GuggariDepartment of Computer ApplicationsBMS College of EngineeringBengaluruKarnatakaIndia

Sailesh Kumar GuptaDarjeeling Government CollegeDarjeelingWest BengalIndia

Glenn HarrisDepartment of Mathematical SciencesNorthern Illinois UniversityDeKalb, ILUSA

Zar Chi Su Su HlaingUniversity of Computer Studies (Magway)MagwayMyanmar

Nadeem Ahmad KhanJamia Millia IslamiaNew DelhiIndia

Vandana KhareCMR College of Engineering and TechnologyHyderabadTelanganaIndia

Myo KhaingUniversity of Computer Studies (Magway)MagwayMyanmar


xxi

Ajay KulkarniMedi-Caps UniversityIndoreMadhya PradeshIndia

Rahul KumarDepartment of Information TechnologyNational Institute of Technology RaipurRaipurChhattisgarhIndia

Bonya MukherjeeBhilai Steel PlantBhilaiChhattisgarhIndia

Kapil Kumar NagwanshiMPSTME Shirpur CampusSVKM’s NMIMS UniversityMumbaiMaharashtraIndia

Pushkala NarasimhanPG Department of CommerceNMKRV College for WomenBangaloreKarnatakaIndia

Jyotiprakash PatraSSTC-SSGICSVTUBhilaiChhattisgarhIndia

Jyothi PillaiDepartment of Information TechnologyBhilai Institute of TechnologyDurgChhattisgarhIndia


xxii

Rajendra PrasadIndian Institute of Technology RoorkeeRoorkeeUttarakhandIndia

Sachin PuntambekarMedi-Caps UniversityIndoreMadhya PradeshIndia

Subrahmanian RamaniBhilai Steel PlantBhilaiChhattisgarhIndia

K C RaveendranathanRajadhani Institute of Engineering and TechnologyThiruvananthapuramKeralaIndia

Arpana RawalBhilai Institute of TechnologyDurgChhattisgarhIndia

Mridu SahuDepartment of Information TechnologyNational Institute of Technology RaipurRaipurChhattisgarhIndia

Mamta SinghSai CollegeBhilaiChhattisgarhIndia

G R SinhaMyanmar Institute of Information TechnologyMandalayMyanmar


xxiii

Kostiantyn TkachukIgor Sikorsky Kyiv Polytechnic InstituteKievUkraine

Oksana TverdaIgor Sikorsky Kyiv Polytechnic InstituteKievUkraine

Sergij VambolState Ecological Academy of Postgraduate Education and ManagementBerdyansk State Pedagogical UniversityBerdyanskUkraine

Viola VambolBerdyansk State Pedagogical UniversityBerdyanskUkraine

K A VenkateshMyanmar Institute of Information TechnologyMandalayMyanmar


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