Optimization Techniques in Engineering
Description
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.
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Persons
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.
Content
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 gsa*-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 gsa* - Continuous Function 263
16.4 Almost Neutrosophic gsa* - 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
Preface
Optimization is a precise method that allows the planner to identify the best solution to a problem by using design restrictions and criteria. Optimization techniques have been used to solve a variety of practical problems in a variety of fields. These optimization methods have existed since the time of Newton, Lagrange and Cauchy. The contributions of Leibnitz and Newton to calculus are responsible for the growth of differential calculus approaches for optimization. Optimization techniques are useful tools for obtaining the required design parameters and operational conditions. They direct the experimental effort and lower the design and operations risk and cost. Finding the values of decision variables that correspond to and give the maximum or minimum of one or more specified objectives is referred to as optimization. An optimization algorithm is a process that compares numerous solutions iteratively until an optimum or satisfying solution is identified. Thus, optimization has become a feature of computer-aided design activities since the invention of computers. The formulation of goal functions and the optimization technique chosen determine the reliability of optimal solutions. A mathematical model that characterizes and predicts the process behavior is required for optimization. An optimization search could aid in the estimation of unknown parameters in complex nonlinear processes. In dynamic processes, robust optimization can be used to find uncertainty variables. Optimization can also be used to aid in the development of scale-up methodologies and the design of multiphase reactors and flow systems. Manufacturing and engineering activities currently being used will not be as efficient until design and operations are optimized. The purpose of design optimization might simply be to reduce production costs or to increase production efficiency. Optimization is particularly important in companies since it helps to cut costs, which can lead to increased earnings and success in a competitive environment.
There are two types of optimization approaches used: traditional and soft computing-based. Traditional optimization techniques can be used to identify the best solution or unconstrained maxima and minima of continuous and differentiable functions. These are mathematical methods that use differential calculus to get the best solution. Optimization can decrease readability and introduce code that is only needed to boost performance. This might make programs or systems more difficult to maintain and debug. As a result, performance tweaking or optimization is frequently done near the conclusion of the development stage. Customer experience optimization is critical since it boosts customer satisfaction and helps organizations improve their key performance indicators. More revenue and growth are frequently associated with satisfied customers and improved key performance indicators. Performance optimization is the practice of altering how a system functions to increase its efficiency and effectiveness. Debugging the optimization solution is more complex than debugging the rule-based simulation solution. It could be a mix of numerous limitations and input data from multiple places and time steps. Many fields have used optimization theory and methodologies to solve various practical challenges. Soft computing is the application of approximation computations to difficult computer problems to produce "mushy" but usable outcomes. The method produces results for issues that are either unsolvable or take too long to solve using traditional methods.
An optimization algorithm is a process that compares numerous solutions iteratively until an optimum or satisfying one is identified. It follows particular rules when moving from one solution to the next. Many fields of study employ optimization methods to find solutions that maximize or minimize certain study parameters, such as minimizing expenses in the manufacture of a thing or service, maximizing earnings, minimizing raw materials in the development of a good, or maximizing productivity.
The goal of optimizing an objective function is to identify a set of inputs that results in a maximum or minimal function evaluation. Many machine learning algorithms, from fitting logistic regression models to training artificial neural networks, are based on this concept. The purpose of the optimization process is to find choice variable values that result in an objective function's maximum or minimum. Optimization issues are characterized as linear, nonlinear, geometric, or quadratic programming problems based on the nature of the expressions for the objective function and constraints. An optimization model is a representation of the key features of the business problem being addressed. The objective function, decision variables, and business constraints are the three components of the model.
The purpose of optimization is to find the best design possible based on a set of prioritized criteria or constraints. These include, among other things, boosting productivity, strength, reliability, lifetime, efficiency, and utilization. Many fields have used optimization theory and methodologies to solve various practical challenges; and optimization techniques have become increasingly important and popular in various engineering applications as computing systems have advanced.
The aim of this book is 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 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. A brief description of each of the chapters is presented below:
Part I: Soft Computing and Evolutionary-Based Optimization
- Chapter 1 attempts to realistically represent the existing grey wolf optimizer in order to increase the algorithm's efficiency. Unlike grey wolf optimizers, the modeling of prey in this study is considered dynamic. To solve a dynamic economic dispatch problem with electric vehicle profiles, dynamism is added to the prey using the Levy flight distribution function.
- Chapter 2 presents an object detection approach devised for detecting plastics. To apply transfer learning, the algorithm is written in tensor flow. The two most commonly used object identification approaches, YOLO and Faster R-CNN, are compared.
- Chapter 3 strives to increase the use of real power in relation to reactive power in order to improve the power factor. The power factor is corrected closer to unity with the help of a smart power factor correction device. The power factor controller swaps the appropriate capacitor blocks in steps depending on the load and power factor of the network to raise the power factor to almost 0.95. MATLAB software is used to simulate the system.
- Chapter 4 focuses on the design and analysis of a solar-fed cascaded boost converter with a maximum power point tracking (MPPT) algorithm based on neural networks (NN), which is used for electric vehicle (EV) applications.
- Chapter 5 proposes modeling of a single and multi-junction solar cell with maximum power point tracking (MPPT) using an intelligent fuzzy logic algorithm (FLA) for maximum productivity.
- Chapter 6 discusses the utility of particle swarm optimization (PSO), as well as the weaknesses in the algorithm. The latest developments and improvements to the PSO parameters are also highlighted. Finally, it explores its hybridization with other notable algorithms and applications in a variety of disciplines and contexts during the last few decades.
- Chapter 7 delves into the uniformity subfields of sensor networks, as well as energy-efficient sensor networks, which actively investigate the use of genetic algorithms (GAs) as a fundamental component in developing deployment strategies and routing protocols. Since manual garbage collection is a time-consuming technique that cannot keep up with the ever-increasing demands of city rubbish, sensor networks can be used in garbage collection as the first step toward proper plastics treatment and recycling for a more environmentally friendly future.
- Chapter 8 presents several machine learning methods, such as random forests, linear regression, XGBoost, and support vector machine (SVM), that aid in the estimation of delamination factor based on known inputs such as feed rate, point angle, and spindle speed.
- Chapter 9 attempts using a differential evolutionary algorithm to perform sand elevation categorization of sand deposits acquired with a drone camera in a desert location. However, the results are unsatisfactory, prompting the development of an evolutionary algorithm-based contour identification approach for accurate elevation categorization.
- Chapter 10 mainly discusses promoting energy efficiency while maintaining a sustainable minimum spectral efficiency requirement. By obtaining the Pareto optimal solution set, the joint user clustering and multi-objective particle swarm optimization (MOPSO) method has been proposed for downlink non-orthogonal multiple access (NOMA). The simulation results reveal that the proposed MOPSO approach outperforms the existing systems in terms of parametric efficiency.
- Chapter 11 uses the Amazon fine food reviews dataset to automatically analyze product reviews and classify them as good or terrible. Four distinct types of classifiers were employed to identify the reviews...
