Optimization Techniques in Engineering

Anita Khosla, PhD, is a professor in the Department of Electrical and Electronics Engineering at Manav Rachna International Institute of Research and Studies, University, Faridabad. She is the editor of two books and more than 50 research papers in national, international journals and conferences. Prasenjit Chatterjee, PhD, is a full professor of Mechanical Engineering and Dean (Research and Consultancy) at MCKV Institute of Engineering, West Bengal, India. He has more than 120 research papers in various international journals and peer-reviewed conferences. He has authored and edited more than 22 books on intelligent decision-making, fuzzy computing, supply chain management, optimization techniques, risk management, and sustainability modeling. Dr. Chatterjee is one of the developers of a new multiple-criteria decision-making method called Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS). Ikbal Ali, PhD, is a professor in the Department of Electrical Engineering, Faculty of Engineering & Technology of Jamia Millia Islamia, New Delhi, India. His research work has been widely published and cited in refereed international journals/conferences of repute like IEEE. His research interests are in the fields of power systems, operation, and control; and smart grid technologies. Dheeraj Joshi, PhD, is a professor in the Electrical Engineering Department, Delhi Technological University since 2015. He has published more than 200 publications in international/national journals and conferences. His areas of interest are power electronics converters, induction generators in wind energy conversion systems, and electric drives.

• Preface xxiAcknowledgment xxixPart 1: Soft Computing and Evolutionary-Based Optimization 11 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles 3Anjali Jain, Ashish Mani and Anwar S. Siddiqui1.1 Introduction 41.2 Problem Formulation 51.2.1 Power Output Limits 61.2.2 Power Balance Limits 61.2.3 Ramp Rate Limits 71.2.4 Electric Vehicles 71.3 Proposed Algorithm 81.3.1 Overview of Grey Wolf Optimizer 81.3.2 Improved Grey Wolf Optimizer with Levy Flight 91.3.3 Modeling of Prey Position with Levy Flight Distribution 101.4 Simulation and Results 131.4.1 Performance of Improved GWOLF on Benchmark Functions 141.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution 141.5 Conclusion 29References 34xxivii2 Comparison of YOLO and Faster R-CNN on Garbage Detection 37Arulmozhi M., Nandini G. Iyer, Jeny Sophia S., Sivakumar P., Amutha C. and Sivamani D.2.1 Introduction 372.2 Garbage Detection 392.2.1 Transfer Learning-Technique 392.2.2 Inception-Custom Model 392.2.2.1 Convolutional Neural Network 402.2.2.2 Max Pooling 412.2.2.3 Stride 412.2.2.4 Average Pooling 412.2.2.5 Inception Layer 422.2.2.6 3*3 and 1*1 Convolution 432.2.2.7 You Only Look Once (YOLO) Architecture 432.2.2.8 Faster R-CNN Algorithm 442.2.2.9 Mean Average Precision (mAP) 462.3 Experimental Results 462.3.1 Results Obtained Using YOLO Algorithm 462.3.2 Results Obtained Using Faster R-CNN 462.4 Future Scope 482.5 Conclusion 48References 483 Smart Power Factor Correction and Energy Monitoring System 51Amutha C., Sivagami V., Arulmozhi M., Sivamani D. and Shyam D.3.1 Introduction 513.2 Block Diagram 533.2.1 Power Factor Concept 543.2.2 Power Factor Calculation 543.3 Simulation 543.4 Conclusion 56References 574 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications 59Sivamani D., Sangari A., Shyam D., Anto Sheeba J., Jayashree K. and Nazar Ali A.4.1 Introduction 594.2 Block Diagram 604.3 ANN-Based MPPT for Boost Converter 644.4 Closed Loop Control 664.5 Simulation Results 674.6 Conclusion 70References 705 Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm 73Omveer Singh, Shalini Gupta and Shabana Urooj5.1 Introduction 745.2 Modeling Structure 755.2.1 Single-Junction Solar Cell Model 755.2.2 Modeling of Multijunction Solar PV Cell 775.3 MPPT Design Techniques 805.3.1 Design of MPPT Scheme Based on P&O Technique 805.3.2 Design of MPPT Scheme Based on FLA 825.4 Results and Discussions 845.4.1 Single-Junction Solar Cell 845.4.2 Multijunction Solar PV Cell 865.4.3 Implementation of MPPT Scheme Based on P&O Technique 905.4.4 Implementation of MPPT Scheme Based on FLA 915.5 Conclusion 93References 936 Particle Swarm Optimization: An Overview, Advancements and Hybridization 95Shafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui and Prashant6.1 Introduction 966.2 The Particle Swarm Optimization: An Overview 976.3 PSO Algorithms and Pseudo-Code 986.3.1 PSO Algorithm 986.3.2 Pseudo-Code for PSO 1016.3.3 PSO Limitations 1016.4 Advancements in PSO and Its Perspectives 1026.4.1 Inertia Weight 1026.4.1.1 Random Selection (RS) 1026.4.1.2 Linear Time Varying (LTV) 1036.4.1.3 Nonlinear Time Varying (NLTV) 1036.4.1.4 Fuzzy Adaptive (FA) 1036.4.2 Constriction Factors 1046.4.3 Topologies 1046.4.4 Analysis of Convergence 1046.5 Hybridization of PSO 1056.5.1 PSO Hybridization with Artificial Bee Colony (ABC) 1056.5.2 PSO Hybridization with Ant Colony Optimization (aco) 1066.5.3 PSO Hybridization with Genetic Algorithms (GA) 1066.6 Area of Applications of PSO 1076.7 Conclusions 109References 1097 Application of Genetic Algorithm in Sensor Networks and Smart Grid 115Geeta Yadav, Dheeraj Joshi, Leena G. and M. K. Soni7.1 Introduction 1157.2 Communication Sector 1167.2.1 Sensor Networks 1167.3 Electrical Sector 1177.3.1 Smart Microgrid 1177.4 A Brief Outline of GAs 1187.5 Sensor Network’s Energy Optimization 1207.6 Sensor Network’s Coverage and Uniformity Optimization Using GA 1267.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid 1317.8 GA Versus Traditional Methods 1357.9 Summaries and Conclusions 136References 1378 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber–Reinforced Polymer (CFRP) Drilling Process 139Rohit Volety and Geetha Mani8.1 Introduction 1408.2 Methodology 1428.3 AI-Based Predictive Modeling 1438.3.1 Linear Regression 1438.3.2 Random Forests 1448.3.3 XGBoost 1458.3.4 Svm 1468.4 Performance Indices 1468.4.1 Root Mean Squared Error (RMSE) 1468.4.2 Mean Squared Error (MSE) 1478.4.3 R 2 (R-Squared) 1478.5 Results and Discussion 1478.5.1 Key Performance Metrics (KPIs) During the Model Training Phase 1488.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase 1488.5.3 K Cross Fold Validation 1498.6 Conclusions 151References 1529 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery 155Jacob Vishal, Somdeb Datta, Sudipta Mukhopadhyay, Pravar Kulbhushan, Rik Das, Saurabh Srivastava and Indrajit Kar9.1 Introduction 1569.2 Literature Survey 1579.3 Research Methodology 1599.3.1 Dataset and Metrics 1619.4 Result and Discussion 1629.5 Conclusion 165References 16510 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA 169Jyotirmayee Subudhi and P. Indumathi10.1 Introduction 17010.2 System Model 17210.3 User Clustering 17510.4 Optimal Power Allocation for EE-SE Tradeoff 17610.4.1 Multiobjective Optimization Problem 17710.4.2 Multiobjective PSO 17810.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA 18010.5 Numerical Results 18010.6 Conclusion 183References 18411 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews 187Rishabh Singh, Akarshan Kumar and Mousim Ray11.1 Introduction 18811.1.1 Related Work 18911.2 Materials and Methods 19011.2.1 Data Cleaning and Pre-Processing 19111.2.2 Feature Extraction 19111.2.3 Classifiers 19311.3 Results and Experiments 19411.4 Conclusion 197References 19812 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm 201Mintu Pal and Sibsankar Dasmahapatra12.1 Introduction 20212.2 Genetic Algorithm GA: An Evolutionary Computational Technique 20312.3 Design of Multiobjective Optimization Problem 20412.3.1 Decision Variables 20412.3.2 Objective Functions 20412.3.2.1 Minimization of Main Cutting Force 20512.3.2.2 Minimization of Feed Force 20512.3.3 Bounds of Decision Variables 20512.3.4 Response Variables 20612.4 Results and Discussions 20612.4.1 Single Objective Optimization 20612.4.2 Results of Multiobjective Optimization 20812.5 Conclusion 212References 21213 Genetic Algorithm-Based Optimization for Speech Processing Applications 215Ramya.R, M. Preethi and R. Rajalakshmi13.1 Introduction to GA 21513.1.1 Enhanced GA 21613.1.1.1 Hybrid GA 21613.1.1.2 Interval GA 21713.1.1.3 Adaptive GA 21713.2 GA in Automatic Speech Recognition 21813.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD) 21813.2.2 Classification of Features in ASR Using GA 21913.2.3 GA-Based Distinctive Phonetic Features Recognition 21913.2.4 GA in Phonetic Decoding 22013.3 Genetic Algorithm in Speech Emotion Recognition 22113.3.1 Speech Emotion Recognition 22113.3.2 Genetic Algorithms in Speech Emotion Recognition 22213.3.2.1 Feature Extraction Using GA for SER 22213.3.2.2 Steps for Adaptive Genetic Algorithm for Feature Optimization 22413.4 Genetic Programming in Hate Speech Using Deep Learning 22513.4.1 Introduction to Hate Speech Detection 22513.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection 22613.5 Conclusion 228References 22814 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant 231Ranjit Singh and L. Ramesh14.1 Introduction 23114.2 Single-Area Power System 23214.3 Automatic Load Frequency Control (ALFC) 23314.4 Controllers Used in the Simulink Model 23314.4.1 PID Controller 23314.4.2 PI Controller 23414.4.3 P Controller 23414.5 Circuit Description 23514.6 ANN and NARMA L2 Controller 23614.7 Simulation Results and Comparative Analysis 23714.8 Conclusion 239References 240Part 2: Decision Science and Simulation-Based Optimization 24315 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment 245Bipradas Bairagi15.1 Introduction 24615.2 Fuzzy Set Theory 24815.2.1 Some Important Fuzzy Definitions 24815.2.2 Fuzzy Operations 24915.2.3 Linguistic Variable (LV) 25015.3 Fvikor 25115.4 Problem Definition 25315.5 Results and Discussions 25315.6 Conclusions 258References 25916 Slightly and Almost Neutrosophic gsα*—Continuous Function in Neutrosophic Topological Spaces 261P. Anbarasi Rodrigo and S. Maheswari16.1 Introduction 26116.2 Preliminaries 26216.3 Slightly Neutrosophic gsα* – Continuous Function 26316.4 Almost Neutrosophic gsα* – Continuous Function 26616.5 Conclusion 274References 27417 Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers 275Hullash Chauhan, Suchismita Satapathy, A. K. Sahoo and Debesh Mishra17.1 Introduction 27517.2 Materials and Methods 27717.2.1 ELECTRE Technique 27817.3 Result and Discussion 28117.4 Conclusion 293References 29418 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection 297Bipradas Bairagi18.1 Introduction 29818.2 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): The Proposed Algorithm 30018.3 Illustrative Example 30318.3.1 Problem Definition 30318.3.2 Calculation and Discussions 30518.4 Conclusions 309References 31019 Evaluation of Optimal Parameters to Enhance Worker’s Performance in an Automotive Industry 313Rajat Yadav, Kuwar Mausam, Manish Saraswat and Vijay Kumar Sharma19.1 Introduction 31419.2 Methodology 31519.3 Results and Discussion 31619.4 Conclusions 320References 32120 Determining Key Influential Factors of Rural Tourism— An AHP Model 323Puspalata Mahaptra, RamaKrishna Bandaru, Deepanjan Nanda and Sushanta Tripathy20.1 Introduction 32420.2 Rural Tourism 32520.3 Literature Review 32620.4 Objectives 32820.5 Methodology 32820.6 Analysis 33220.7 Results and Discussion 33220.8 Conclusions 34020.9 Managerial Implications 340References 34121 Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment 345Partha Pratim Bhattacharya, Kousik Bhattacharya, Sujit Kumar De, Prasun Kumar Nayak, Subhankar Joardar and Kushankur Das21.1 Introduction 34621.1.1 Overview 34621.1.2 Motivation and Specific Study 34621.2 Preliminaries 34821.2.1 Pollution Function 34821.2.2 Triangular Dense Fuzzy Set (TDFS) 34921.3 Notations and Assumptions 35021.3.1 Case Study 35121.4 Formulation of the Mathematical Model 35221.4.1 Crisp Mathematical Model 35221.4.2 Formulation of Triangular Dense Fuzzy Mathematical Model 35221.4.3 Defuzzification of Triangular Dense Fuzzy Model 35321.5 Numerical Illustration 35421.6 Sensitivity Analysis 35521.7 Graphical Illustration 35521.8 Merits and Demerits 35821.9 Conclusion 358Acknowledgement 359Appendix 359References 36022 Common Yet Overlooked Aspects Accountable for Antiaging: An MCDM Approach 363Rajnandini Saha, Satyabrata Aich, Hee-Cheol Kim and Sushanta Tripathy22.1 Introduction 36422.2 Literature Review 36522.3 Analytic Hierarchy Process (AHP) 36722.4 Result and Discussion 37222.5 Conclusion 373References 37323 E-Waste Management Challenges in India: An AHP Approach 377Amit Sutar, Apurv Singh, Deepak Singhal, Sushanta Tripathy and Bharat Chandra Routara23.1 Introduction 37823.2 Literature Review 37923.3 Methodology 37923.4 Results and Discussion 37923.5 Conclusion 390References 39124 Application of k-Means Method for Finding Varying Groups of Primary Energy Household Emissions in the Indian States 393Tanmay Belsare, Abhay Deshpande, Neha Sharma and Prithwis De24.1 Introduction 39424.2 Literature Review 39524.3 Materials and Methods 39724.3.1 Data Preparation 39724.3.2 Methods and Approach 39724.3.2.1 Cluster Analysis 39724.3.2.2 Agglomerative Hierarchical Clustering 39724.3.2.3 K-Means Clustering 39824.4 Exploratory Data Analysis 39824.5 Results and Discussion 40124.6 Conclusion 405References 40625 Airwaves Detection and Elimination Using Fast Fourier Transform to Enhance Detection of Hydrocarbon 409Garba Aliyu, Mathias M. Fonkam, Augustine S. Nsang, Muhammad Abdulkarim, Sandip Rashit and Yakub K. Saheed25.1 Introduction 41025.1.1 Airwaves 41125.1.2 Fast Fourier Transform 41225.2 Related Works 41325.3 Theoretical Framework 41525.4 Methodology 41625.5 Results and Discussions 41725.6 Conclusion 420References 42026 Design and Implementation of Control for Nonlinear Active Suspension System 423Ravindra S. Rana and Dipak M. Adhyaru26.1 Introduction 42326.2 Mathematical Model of Quarter Car Suspension System 42626.2.1 Mathematical Model 42626.2.2 Linearization Method for Nonlinear System Model 42926.2.3 Discussion of Result 43026.3 Conclusion 433References 43427 A Study of Various Peak to Average Power Ratio (PAPR) Reduction Techniques for 5G Communication System (5G-CS) 437Himanshu Kumar Sinha, Anand Kumar and Devasis Pradhan27.1 Introduction 43727.2 Literature Review 43927.3 Overview of 5G Cellular System 44027.4 Papr 44127.4.1 Continuous Time PAPR 44127.4.2 Continuous Time PAPR 44227.5 Factors on which PAPR Reduction Depends 44227.6 PAPR Reduction Technique 44327.6.1 Scrambling of Signals 44327.6.2 Signal Distortion Technique 44627.6.3 High Power Amplifier (HPA) 44727.7 Limitation of OFDM 44727.8 Universal Filter Multicarrier (UMFC) Emerging Technique to Reduce PAPR in 5G 44827.8.1 Transmitter of UMFC 44827.8.2 Receiver of UMFC 45027.9 Comparison Between Various Techniques 45027.10 Conclusion 450References 45228 Investigation of Rebound Suppression Phenomenon in an Electromagnetic V-Bending Test 455Aman Sharma, Pradeep Kumar Singh, Manish Saraswat and Irfan Khan28.1 Introduction 45528.2 Investigation 45828.2.1 Specimen for Tests 45828.2.2 Design of Die and Tool 45828.2.3 Configuration and Procedure 45928.3 Mathematical Evaluation 46028.3.1 Simulation Methodology 46028.4 Modeling for Material 46128.4.1 Suppressing Rebound Phenomenon 46128.5 Conclusion 466References 46629 Quadratic Spline Function Companding Technique to Minimize Peak-to-Average Power Ratio in Orthogonal Frequency Division Multiplexing System 469Lazar Z. Velimirovic29.1 Introduction 46929.2 OFDM System 47129.2.1 PAPR of OFDM Signal 47229.3 Companding Technique 47429.3.1 Quadratic Spline Function Companding 47429.4 Numerical Results and Discussion 47529.5 Conclusion 480Acknowledgment 480References 48030 A Novel MCGDM Approach for Supplier Selection in a Supply Chain Management 483Bipradas Bairagi30.1 Introduction 48430.2 Proposed Algorithm 48630.3 Illustrative Example 49130.3.1 Problem Definition 49130.3.2 Calculation and Discussions 49230.4 Conclusions 498References 499Index 501