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Optimization Techniques in Engineering. Advances and Applications. Edition No. 1. Sustainable Computing and Optimization

  • Book

  • 544 Pages
  • May 2023
  • John Wiley and Sons Ltd
  • ID: 5837893
OPTIMIZATION TECHNIQUES IN ENGINEERING

The book describes the basic components of an optimization problem along with the formulation of design problems as mathematical programming problems using an objective function that expresses the main aim of the model, and how it is to be either minimized or maximized; subsequently, the concept of optimization and its relevance towards an optimal solution in engineering applications, is explained.

This book aims to present some of the recent developments in the area of optimization theory, methods, and applications in engineering. It focuses on the metaphor of the inspired system and how to configure and apply the various algorithms. The book comprises 30 chapters and is organized into two parts: Part I - Soft Computing and Evolutionary-Based Optimization; and Part II - Decision Science and Simulation-Based Optimization, which contains application-based chapters.

Readers and users will find in the book: - An overview and brief background of optimization methods which are used very popularly in almost all applications of science, engineering, technology, and mathematics; - An in-depth treatment of contributions to optimal learning and optimizing engineering systems; - Maps out the relations between optimization and other mathematical topics and disciplines; - A problem-solving approach and a large number of illustrative examples, leading to a step-by-step formulation and solving of optimization problems.

Audience

Researchers, industry professionals, academicians, and doctoral scholars in major domains of engineering, production, thermal, electrical, industrial, materials, design, computer engineering, and natural sciences. The book is also suitable for researchers and postgraduate students in mathematics, applied mathematics, and industrial mathematics.

Table of Contents

Preface xxi

Acknowledgment xxix

Part 1: Soft Computing and Evolutionary-Based Optimization 1

1 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles 3
Anjali Jain, Ashish Mani and Anwar S. Siddiqui

1.1 Introduction 4

1.2 Problem Formulation 5

1.2.1 Power Output Limits 6

1.2.2 Power Balance Limits 6

1.2.3 Ramp Rate Limits 7

1.2.4 Electric Vehicles 7

1.3 Proposed Algorithm 8

1.3.1 Overview of Grey Wolf Optimizer 8

1.3.2 Improved Grey Wolf Optimizer with Levy Flight 9

1.3.3 Modeling of Prey Position with Levy Flight Distribution 10

1.4 Simulation and Results 13

1.4.1 Performance of Improved GWOLF on Benchmark Functions 14

1.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution 14

1.5 Conclusion 29

References 34

xxi

vii

2 Comparison of YOLO and Faster R-CNN on Garbage Detection 37
Arulmozhi M., Nandini G. Iyer, Jeny Sophia S., Sivakumar P., Amutha C. and Sivamani D.

2.1 Introduction 37

2.2 Garbage Detection 39

2.2.1 Transfer Learning-Technique 39

2.2.2 Inception-Custom Model 39

2.2.2.1 Convolutional Neural Network 40

2.2.2.2 Max Pooling 41

2.2.2.3 Stride 41

2.2.2.4 Average Pooling 41

2.2.2.5 Inception Layer 42

2.2.2.6 3*3 and 1*1 Convolution 43

2.2.2.7 You Only Look Once (YOLO) Architecture 43

2.2.2.8 Faster R-CNN Algorithm 44

2.2.2.9 Mean Average Precision (mAP) 46

2.3 Experimental Results 46

2.3.1 Results Obtained Using YOLO Algorithm 46

2.3.2 Results Obtained Using Faster R-CNN 46

2.4 Future Scope 48

2.5 Conclusion 48

References 48

3 Smart Power Factor Correction and Energy Monitoring System 51
Amutha C., Sivagami V., Arulmozhi M., Sivamani D. and Shyam D.

3.1 Introduction 51

3.2 Block Diagram 53

3.2.1 Power Factor Concept 54

3.2.2 Power Factor Calculation 54

3.3 Simulation 54

3.4 Conclusion 56

References 57

4 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications 59
Sivamani D., Sangari A., Shyam D., Anto Sheeba J., Jayashree K. and Nazar Ali A.

4.1 Introduction 59

4.2 Block Diagram 60

4.3 ANN-Based MPPT for Boost Converter 64

4.4 Closed Loop Control 66

4.5 Simulation Results 67

4.6 Conclusion 70

References 70

5 Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm 73
Omveer Singh, Shalini Gupta and Shabana Urooj

5.1 Introduction 74

5.2 Modeling Structure 75

5.2.1 Single-Junction Solar Cell Model 75

5.2.2 Modeling of Multijunction Solar PV Cell 77

5.3 MPPT Design Techniques 80

5.3.1 Design of MPPT Scheme Based on P&O Technique 80

5.3.2 Design of MPPT Scheme Based on FLA 82

5.4 Results and Discussions 84

5.4.1 Single-Junction Solar Cell 84

5.4.2 Multijunction Solar PV Cell 86

5.4.3 Implementation of MPPT Scheme Based on P&O Technique 90

5.4.4 Implementation of MPPT Scheme Based on FLA 91

5.5 Conclusion 93

References 93

6 Particle Swarm Optimization: An Overview, Advancements and Hybridization 95
Shafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui and Prashant

6.1 Introduction 96

6.2 The Particle Swarm Optimization: An Overview 97

6.3 PSO Algorithms and Pseudo-Code 98

6.3.1 PSO Algorithm 98

6.3.2 Pseudo-Code for PSO 101

6.3.3 PSO Limitations 101

6.4 Advancements in PSO and Its Perspectives 102

6.4.1 Inertia Weight 102

6.4.1.1 Random Selection (RS) 102

6.4.1.2 Linear Time Varying (LTV) 103

6.4.1.3 Nonlinear Time Varying (NLTV) 103

6.4.1.4 Fuzzy Adaptive (FA) 103

6.4.2 Constriction Factors 104

6.4.3 Topologies 104

6.4.4 Analysis of Convergence 104

6.5 Hybridization of PSO 105

6.5.1 PSO Hybridization with Artificial Bee Colony (ABC) 105

6.5.2 PSO Hybridization with Ant Colony Optimization (aco) 106

6.5.3 PSO Hybridization with Genetic Algorithms (GA) 106

6.6 Area of Applications of PSO 107

6.7 Conclusions 109

References 109

7 Application of Genetic Algorithm in Sensor Networks and Smart Grid 115
Geeta Yadav, Dheeraj Joshi, Leena G. and M. K. Soni

7.1 Introduction 115

7.2 Communication Sector 116

7.2.1 Sensor Networks 116

7.3 Electrical Sector 117

7.3.1 Smart Microgrid 117

7.4 A Brief Outline of GAs 118

7.5 Sensor Network’s Energy Optimization 120

7.6 Sensor Network’s Coverage and Uniformity Optimization Using GA 126

7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid 131

7.8 GA Versus Traditional Methods 135

7.9 Summaries and Conclusions 136

References 137

8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber-Reinforced Polymer (CFRP) Drilling Process 139
Rohit Volety and Geetha Mani

8.1 Introduction 140

8.2 Methodology 142

8.3 AI-Based Predictive Modeling 143

8.3.1 Linear Regression 143

8.3.2 Random Forests 144

8.3.3 XGBoost 145

8.3.4 Svm 146

8.4 Performance Indices 146

8.4.1 Root Mean Squared Error (RMSE) 146

8.4.2 Mean Squared Error (MSE) 147

8.4.3 R 2 (R-Squared) 147

8.5 Results and Discussion 147

8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase 148

8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase 148

8.5.3 K Cross Fold Validation 149

8.6 Conclusions 151

References 152

9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery 155
Jacob Vishal, Somdeb Datta, Sudipta Mukhopadhyay, Pravar Kulbhushan, Rik Das, Saurabh Srivastava and Indrajit Kar

9.1 Introduction 156

9.2 Literature Survey 157

9.3 Research Methodology 159

9.3.1 Dataset and Metrics 161

9.4 Result and Discussion 162

9.5 Conclusion 165

References 165

10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA 169
Jyotirmayee Subudhi and P. Indumathi

10.1 Introduction 170

10.2 System Model 172

10.3 User Clustering 175

10.4 Optimal Power Allocation for EE-SE Tradeoff 176

10.4.1 Multiobjective Optimization Problem 177

10.4.2 Multiobjective PSO 178

10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA 180

10.5 Numerical Results 180

10.6 Conclusion 183

References 184

11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews 187
Rishabh Singh, Akarshan Kumar and Mousim Ray

11.1 Introduction 188

11.1.1 Related Work 189

11.2 Materials and Methods 190

11.2.1 Data Cleaning and Pre-Processing 191

11.2.2 Feature Extraction 191

11.2.3 Classifiers 193

11.3 Results and Experiments 194

11.4 Conclusion 197

References 198

12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm 201
Mintu Pal and Sibsankar Dasmahapatra

12.1 Introduction 202

12.2 Genetic Algorithm GA: An Evolutionary Computational Technique 203

12.3 Design of Multiobjective Optimization Problem 204

12.3.1 Decision Variables 204

12.3.2 Objective Functions 204

12.3.2.1 Minimization of Main Cutting Force 205

12.3.2.2 Minimization of Feed Force 205

12.3.3 Bounds of Decision Variables 205

12.3.4 Response Variables 206

12.4 Results and Discussions 206

12.4.1 Single Objective Optimization 206

12.4.2 Results of Multiobjective Optimization 208

12.5 Conclusion 212

References 212

13 Genetic Algorithm-Based Optimization for Speech Processing Applications 215
Ramya.R, M. Preethi and R. Rajalakshmi

13.1 Introduction to GA 215

13.1.1 Enhanced GA 216

13.1.1.1 Hybrid GA 216

13.1.1.2 Interval GA 217

13.1.1.3 Adaptive GA 217

13.2 GA in Automatic Speech Recognition 218

13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) 218

13.2.2 Classification of Features in ASR Using GA 219

13.2.3 GA-Based Distinctive Phonetic Features Recognition 219

13.2.4 GA in Phonetic Decoding 220

13.3 Genetic Algorithm in Speech Emotion Recognition 221

13.3.1 Speech Emotion Recognition 221

13.3.2 Genetic Algorithms in Speech Emotion Recognition 222

13.3.2.1 Feature Extraction Using GA for SER 222

13.3.2.2 Steps for Adaptive Genetic Algorithm for Feature Optimization 224

13.4 Genetic Programming in Hate Speech Using Deep Learning 225

13.4.1 Introduction to Hate Speech Detection 225

13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection 226

13.5 Conclusion 228

References 228

14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant 231
Ranjit Singh and L. Ramesh

14.1 Introduction 231

14.2 Single-Area Power System 232

14.3 Automatic Load Frequency Control (ALFC) 233

14.4 Controllers Used in the Simulink Model 233

14.4.1 PID Controller 233

14.4.2 PI Controller 234

14.4.3 P Controller 234

14.5 Circuit Description 235

14.6 ANN and NARMA L2 Controller 236

14.7 Simulation Results and Comparative Analysis 237

14.8 Conclusion 239

References 240

Part 2: Decision Science and Simulation-Based Optimization 243

15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment 245
Bipradas Bairagi

15.1 Introduction 246

15.2 Fuzzy Set Theory 248

15.2.1 Some Important Fuzzy Definitions 248

15.2.2 Fuzzy Operations 249

15.2.3 Linguistic Variable (LV) 250

15.3 Fvikor 251

15.4 Problem Definition 253

15.5 Results and Discussions 253

15.6 Conclusions 258

References 259

16 Slightly and Almost Neutrosophic gsα* - Continuous Function in Neutrosophic Topological Spaces 261
P. Anbarasi Rodrigo and S. Maheswari

16.1 Introduction 261

16.2 Preliminaries 262

16.3 Slightly Neutrosophic gsα* - Continuous Function 263

16.4 Almost Neutrosophic gsα* - Continuous Function 266

16.5 Conclusion 274

References 274

17 Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers 275
Hullash Chauhan, Suchismita Satapathy, A. K. Sahoo and Debesh Mishra

17.1 Introduction 275

17.2 Materials and Methods 277

17.2.1 ELECTRE Technique 278

17.3 Result and Discussion 281

17.4 Conclusion 293

References 294

18 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection 297
Bipradas Bairagi

18.1 Introduction 298

18.2 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): The Proposed Algorithm 300

18.3 Illustrative Example 303

18.3.1 Problem Definition 303

18.3.2 Calculation and Discussions 305

18.4 Conclusions 309

References 310

19 Evaluation of Optimal Parameters to Enhance Worker’s Performance in an Automotive Industry 313
Rajat Yadav, Kuwar Mausam, Manish Saraswat and Vijay Kumar Sharma

19.1 Introduction 314

19.2 Methodology 315

19.3 Results and Discussion 316

19.4 Conclusions 320

References 321

20 Determining Key Influential Factors of Rural Tourism - An AHP Model 323
Puspalata Mahaptra, RamaKrishna Bandaru, Deepanjan Nanda and Sushanta Tripathy

20.1 Introduction 324

20.2 Rural Tourism 325

20.3 Literature Review 326

20.4 Objectives 328

20.5 Methodology 328

20.6 Analysis 332

20.7 Results and Discussion 332

20.8 Conclusions 340

20.9 Managerial Implications 340

References 341

21 Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment 345
Partha Pratim Bhattacharya, Kousik Bhattacharya, Sujit Kumar De, Prasun Kumar Nayak, Subhankar Joardar and Kushankur Das

21.1 Introduction 346

21.1.1 Overview 346

21.1.2 Motivation and Specific Study 346

21.2 Preliminaries 348

21.2.1 Pollution Function 348

21.2.2 Triangular Dense Fuzzy Set (TDFS) 349

21.3 Notations and Assumptions 350

21.3.1 Case Study 351

21.4 Formulation of the Mathematical Model 352

21.4.1 Crisp Mathematical Model 352

21.4.2 Formulation of Triangular Dense Fuzzy Mathematical Model 352

21.4.3 Defuzzification of Triangular Dense Fuzzy Model 353

21.5 Numerical Illustration 354

21.6 Sensitivity Analysis 355

21.7 Graphical Illustration 355

21.8 Merits and Demerits 358

21.9 Conclusion 358

Acknowledgement 359

Appendix 359

References 360

22 Common Yet Overlooked Aspects Accountable for Antiaging: An MCDM Approach 363
Rajnandini Saha, Satyabrata Aich, Hee-Cheol Kim and Sushanta Tripathy

22.1 Introduction 364

22.2 Literature Review 365

22.3 Analytic Hierarchy Process (AHP) 367

22.4 Result and Discussion 372

22.5 Conclusion 373

References 373

23 E-Waste Management Challenges in India: An AHP Approach 377
Amit Sutar, Apurv Singh, Deepak Singhal, Sushanta Tripathy and Bharat Chandra Routara

23.1 Introduction 378

23.2 Literature Review 379

23.3 Methodology 379

23.4 Results and Discussion 379

23.5 Conclusion 390

References 391

24 Application of k-Means Method for Finding Varying Groups of Primary Energy Household Emissions in the Indian States 393
Tanmay Belsare, Abhay Deshpande, Neha Sharma and Prithwis De

24.1 Introduction 394

24.2 Literature Review 395

24.3 Materials and Methods 397

24.3.1 Data Preparation 397

24.3.2 Methods and Approach 397

24.3.2.1 Cluster Analysis 397

24.3.2.2 Agglomerative Hierarchical Clustering 397

24.3.2.3 K-Means Clustering 398

24.4 Exploratory Data Analysis 398

24.5 Results and Discussion 401

24.6 Conclusion 405

References 406

25 Airwaves Detection and Elimination Using Fast Fourier Transform to Enhance Detection of Hydrocarbon 409
Garba Aliyu, Mathias M. Fonkam, Augustine S. Nsang, Muhammad Abdulkarim, Sandip Rashit and Yakub K. Saheed

25.1 Introduction 410

25.1.1 Airwaves 411

25.1.2 Fast Fourier Transform 412

25.2 Related Works 413

25.3 Theoretical Framework 415

25.4 Methodology 416

25.5 Results and Discussions 417

25.6 Conclusion 420

References 420

26 Design and Implementation of Control for Nonlinear Active Suspension System 423
Ravindra S. Rana and Dipak M. Adhyaru

26.1 Introduction 423

26.2 Mathematical Model of Quarter Car Suspension System 426

26.2.1 Mathematical Model 426

26.2.2 Linearization Method for Nonlinear System Model 429

26.2.3 Discussion of Result 430

26.3 Conclusion 433

References 434

27 A Study of Various Peak to Average Power Ratio (PAPR) Reduction Techniques for 5G Communication System (5G-CS) 437
Himanshu Kumar Sinha, Anand Kumar and Devasis Pradhan

27.1 Introduction 437

27.2 Literature Review 439

27.3 Overview of 5G Cellular System 440

27.4 Papr 441

27.4.1 Continuous Time PAPR 441

27.4.2 Continuous Time PAPR 442

27.5 Factors on which PAPR Reduction Depends 442

27.6 PAPR Reduction Technique 443

27.6.1 Scrambling of Signals 443

27.6.2 Signal Distortion Technique 446

27.6.3 High Power Amplifier (HPA) 447

27.7 Limitation of OFDM 447

27.8 Universal Filter Multicarrier (UMFC) Emerging Technique to Reduce PAPR in 5G 448

27.8.1 Transmitter of UMFC 448

27.8.2 Receiver of UMFC 450

27.9 Comparison Between Various Techniques 450

27.10 Conclusion 450

References 452

28 Investigation of Rebound Suppression Phenomenon in an Electromagnetic V-Bending Test 455
Aman Sharma, Pradeep Kumar Singh, Manish Saraswat and Irfan Khan

28.1 Introduction 455

28.2 Investigation 458

28.2.1 Specimen for Tests 458

28.2.2 Design of Die and Tool 458

28.2.3 Configuration and Procedure 459

28.3 Mathematical Evaluation 460

28.3.1 Simulation Methodology 460

28.4 Modeling for Material 461

28.4.1 Suppressing Rebound Phenomenon 461

28.5 Conclusion 466

References 466

29 Quadratic Spline Function Companding Technique to Minimize Peak-to-Average Power Ratio in Orthogonal Frequency Division Multiplexing System 469
Lazar Z. Velimirovic

29.1 Introduction 469

29.2 OFDM System 471

29.2.1 PAPR of OFDM Signal 472

29.3 Companding Technique 474

29.3.1 Quadratic Spline Function Companding 474

29.4 Numerical Results and Discussion 475

29.5 Conclusion 480

Acknowledgment 480

References 480

30 A Novel MCGDM Approach for Supplier Selection in a Supply Chain Management 483
Bipradas Bairagi

30.1 Introduction 484

30.2 Proposed Algorithm 486

30.3 Illustrative Example 491

30.3.1 Problem Definition 491

30.3.2 Calculation and Discussions 492

30.4 Conclusions 498

References 499

Index 501

Authors

Anita Khosla Manav Rachna International Institute of Research and Studies, University, Faridabad. Prasenjit Chatterjee MCKV Institute of Engineering, West Bengal, India. Ikbal Ali Jamia Millia Islamia, New Delhi, India. Dheeraj Joshi Delhi Technological University.