Optimization Techniques in Engineering - Advances and Applications

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.

List 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
 

About the author

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.

Summary

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.

Product details

• Authors: Ikbal Ali, Prasenjit Chatterjee, Dheeraj Joshi, Khosla, Anita Khosla, Anita (Manav Rachna International Institut Khosla, Anita Chatterjee Khosla
• Assisted by: Ikbal Ali (Editor), Ikbal Ali et al (Editor), Prasenjit Chatterjee (Editor), Dheeraj Joshi (Editor), Anita Khosla (Editor)
• Publisher: Wiley, John and Sons Ltd
• Languages: English
• Product format: Hardback
• Released: 28.02.2023
• EAN: 9781119906278
• ISBN: 978-1-119-90627-8
• No. of pages: 544
• Series: Sustainable Computing and Optimization; Sustainable Computing and Opti
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > IT; Maschinenbau, Informatik, Optimierung, Künstliche Intelligenz, KI, Artificial Intelligence, AI, computer science, Mechanical Engineering, Electrical & Electronics Engineering, Elektrotechnik u. Elektronik, Allg. Maschinenbau, Intelligente Systeme u. Agenten, Intelligent Systems & Agents