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