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Optimization Techniques in Engineering

Advances and Applications 

Edited by Anita Khosla, Prasenjit Chatterjee, Ikbal Ali and Dheeraj Joshi
Series: Sustainable Computing and Optimization
Copyright: 2022   |   Status: Published
ISBN: 9781119906278  |  Hardcover  |  
540 pages | 150 illustrations
Price: $225 USD
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One Line 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.

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.

Description
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.

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Author / Editor Details
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.

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Table of Contents
Preface
Acknowledgment
Part 1: Soft Computing and Evolutionary-Based Optimization
1. Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles
Anjali Jain, Ashish Mani and Anwar S. Siddiqui
1.1 Introduction
1.2 Problem Formulation
1.2.1 Power Output Limits
1.2.2 Power Balance Limits
1.2.3 Ramp Rate Limits
1.2.4 Electric Vehicles
1.3 Proposed Algorithm
1.3.1 Overview of Grey Wolf Optimizer
1.3.2 Improved Grey Wolf Optimizer with Levy Flight
1.3.3 Modeling of Prey Position with Levy Flight Distribution
1.4 Simulation and Results
1.4.1 Performance of Improved GWOLF on Benchmark Functions
1.4.2 Performance of Improved GWOLF for Solving DED for the Different Charging Probability Distribution
1.5 Conclusion
References
2. Comparison of YOLO and Faster R-CNN on Garbage Detection
Arulmozhi M., Nandini G. Iyer, Jeny Sophia S., Sivakumar P., Amutha C. and Sivamani D.
2.1 Introduction
2.2 Garbage Detection
2.2.1 Transfer Learning-Technique
2.2.2 Inception-Custom Model
2.2.2.1 Convolutional Neural Network
2.2.2.2 Max Pooling
2.2.2.3 Stride
2.2.2.4 Average Pooling
2.2.2.5 Inception Layer
2.2.2.6 3*3 and 1*1 Convolution
2.2.2.7 You Only Look Once (YOLO) Architecture
2.2.2.8 Faster R-CNN Algorithm
2.2.2.9 Mean Average Precision (mAP)
2.3 Experimental Results
2.3.1 Results Obtained Using YOLO Algorithm
2.3.2 Results Obtained Using Faster R-CNN
2.4 Future Scope
2.5 Conclusion
References
3. Smart Power Factor Correction and Energy Monitoring System
Amutha C., Sivagami V., Arulmozhi M., Sivamani D. and Shyam D.
3.1 Introduction
3.2 Block Diagram
3.2.1 Power Factor Concept
3.2.2 Power Factor Calculation
3.3 Simulation
3.4 Conclusion
References
4. ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications
Sivamani D., Sangari A., Shyam D., Anto Sheeba J., Jayashree K. and Nazar Ali A.
4.1 Introduction
4.2 Block Diagram
4.3 ANN-Based MPPT for Boost Converter
4.4 Closed Loop Control
4.5 Simulation Results
4.6 Conclusion
References
5. Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm
Omveer Singh, Shalini Gupta and Shabana Urooj
5.1 Introduction
5.2 Modeling Structure
5.2.1 Single-Junction Solar Cell Model
5.2.2 Modeling of Multijunction Solar PV Cell
5.3 MPPT Design Techniques
5.3.1 Design of MPPT Scheme Based on P&O Technique
5.3.2 Design of MPPT Scheme Based on FLA
5.4 Results and Discussions
5.4.1 Single-Junction Solar Cell
5.4.2 Multijunction Solar PV Cell
5.4.3 Implementation of MPPT Scheme Based on P&O Technique
5.4.4 Implementation of MPPT Scheme Based on FLA
5.5 Conclusion
References
6. Particle Swarm Optimization: An Overview, Advancements and Hybridization
Shafquat Rana, Md Sarwar, Anwar Shahzad Siddiqui and Prashant
6.1 Introduction
6.2 The Particle Swarm Optimization: An Overview
6.3 PSO Algorithms and Pseudo-Code
6.3.1 PSO Algorithm
6.3.2 Pseudo-Code for PSO
6.3.3 PSO Limitations
6.4 Advancements in PSO and Its Perspectives
6.4.1 Inertia Weight
6.4.1.1 Random Selection (RS)
6.4.1.2 Linear Time Varying (LTV)
6.4.1.3 Nonlinear Time Varying (NLTV)
6.4.1.4 Fuzzy Adaptive (FA)
6.4.2 Constriction Factors
6.4.3 Topologies
6.4.4 Analysis of Convergence
6.5 Hybridization of PSO
6.5.1 PSO Hybridization with Artificial Bee Colony (ABC)
6.5.2 PSO Hybridization with Ant Colony Optimization (ACO)
6.5.3 PSO Hybridization with Genetic Algorithms (GA)
6.6 Area of Applications of PSO
6.7 Conclusions
References
7. Application of Genetic Algorithm in Sensor Networks and Smart Grid
Geeta Yadav, Dheeraj Joshi, Leena G. and M. K. Soni
7.1 Introduction
7.2 Communication Sector
7.2.1 Sensor Networks
7.3 Electrical Sector
7.3.1 Smart Microgrid
7.4 A Brief Outline of GAs
7.5 Sensor Network’s Energy Optimization
7.6 Sensor Network’s Coverage and Uniformity Optimization Using GA
7.7 Use GA for Optimization of Reliability and Availability for Smart Microgrid
7.8 GA Versus Traditional Methods
7.9 Summaries and Conclusions
References
8. AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber–Reinforced Polymer (CFRP) Drilling Process
Rohit Volety and Geetha Mani
8.1 Introduction
8.2 Methodology
8.3 AI-Based Predictive Modeling
8.3.1 Linear Regression
8.3.2 Random Forests
8.3.3 XGBoost
8.3.4 SVM
8.4 Performance Indices
8.4.1 Root Mean Squared Error (RMSE)
8.4.2 Mean Squared Error (MSE)
8.4.3 R2 (R-Squared)
8.5 Results and Discussion
8.5.1 Key Performance Metrics (KPIs) During the Model Training Phase
8.5.2 Key Performance Index Metrics (KPIs) During the Model Testing Phase
8.5.3 K Cross Fold Validation
8.6 Conclusions
References
9. Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery
Jacob Vishal, Somdeb Datta, Sudipta Mukhopadhyay, Pravar Kulbhushan, Rik Das, Saurabh Srivastava and Indrajit Kar
9.1 Introduction
9.2 Literature Survey
9.3 Research Methodology
9.3.1 Dataset and Metrics
9.4 Result and Discussion
9.5 Conclusion
References
10. Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA
Jyotirmayee Subudhi and P. Indumathi
10.1 Introduction
10.2 System Model
10.3 User Clustering
10.4 Optimal Power Allocation for EE-SE Tradeoff
10.4.1 Multiobjective Optimization Problem
10.4.2 Multiobjective PSO
10.4.3 MOPSO Algorithm for EE-SE Trade-Off in Downlink NOMA
10.5 Numerical Results
10.6 Conclusion
References
11. Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews
Rishabh Singh, Akarshan Kumar and Mousim Ray
11.1 Introduction
11.1.1 Related Work
11.2 Materials and Methods
11.2.1 Data Cleaning and Pre-Processing
11.2.2 Feature Extraction
11.2.3 Classifiers
11.3 Results and Experiments
11.4 Conclusion
References
12. Optimization of Cutting Parameters for Turning by Using Genetic Algorithm
Mintu Pal and Sibsankar Dasmahapatra
12.1 Introduction
12.2 Genetic Algorithm GA: An Evolutionary Computational Technique
12.3 Design of Multiobjective Optimization Problem
12.3.1 Decision Variables
12.3.2 Objective Functions
12.3.2.1 Minimization of Main Cutting Force
12.3.2.2 Minimization of Feed Force
12.3.3 Bounds of Decision Variables
12.3.4 Response Variables
12.4 Results and Discussions
12.4.1 Single Objective Optimization
12.4.2 Results of Multiobjective Optimization
12.5 Conclusion
References
13. Genetic Algorithm-Based Optimization for Speech Processing Applications
Ramya.R, M. Preethi and R. Rajalakshmi
13.1 Introduction to GA
13.1.1 Enhanced GA
13.1.1.1 Hybrid GA
13.1.1.2 Interval GA
13.1.1.3 Adaptive GA
13.2 GA in Automatic Speech Recognition
13.2.1 GA for Optimizing Off-Line Parameters in Voice Activity Detection (VAD)
13.2.2 Classification of Features in ASR Using GA
13.2.3 GA-Based Distinctive Phonetic Features Recognition
13.2.4 GA in Phonetic Decoding
13.3 Genetic Algorithm in Speech Emotion Recognition
13.3.1 Speech Emotion Recognition
13.3.2 Genetic Algorithms in Speech Emotion Recognition
13.3.2.1 Feature Extraction Using GA for SER
13.3.2.2 Steps for Adaptive Genetic Algorithm for Feature Optimization
13.4 Genetic Programming in Hate Speech Using Deep Learning
13.4.1 Introduction to Hate Speech Detection
13.4.2 GA Integrated With Deep Learning Models for Hate Speech Detection
13.5 Conclusion
References
14. Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant
Ranjit Singh and L. Ramesh
14.1 Introduction
14.2 Single-Area Power System
14.3 Automatic Load Frequency Control (ALFC)
14.4 Controllers Used in the Simulink Model
14.4.1 PID Controller
14.4.2 PI Controller
14.4.3 P Controller
14.5 Circuit Description
14.6 ANN and NARMA L2 Controller
14.7 Simulation Results and Comparative Analysis
14.8 Conclusion
References
Part 2: Decision Science and Simulation-Based Optimization
15. Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment
Bipradas Bairagi
15.1 Introduction
15.2 Fuzzy Set Theory
15.2.1 Some Important Fuzzy Definitions
15.2.2 Fuzzy Operations
15.2.3 Linguistic Variable (LV)
15.3 FVIKOR
15.4 Problem Definition
15.5 Results and Discussions
15.6 Conclusions
References
16. Slightly and Almost Neutrosophic gsα*—Continuous Function in Neutrosophic Topological Spaces
P. Anbarasi Rodrigo and S. Maheswari
16.1 Introduction
16.2 Preliminaries
16.3 Slightly Neutrosophic gsα* – Continuous Function
16.4 Almost Neutrosophic gsα* – Continuous Function
16.5 Conclusion
References
17. Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers
Hullash Chauhan, Suchismita Satapathy, A. K. Sahoo and Debesh Mishra
17.1 Introduction
17.2 Materials and Methods
17.2.1 ELECTRE Technique
17.3 Result and Discussion
17.4 Conclusion
References
18. Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection
Bipradas Bairagi
18.1 Introduction
18.2 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): The Proposed Algorithm
18.3 Illustrative Example
18.3.1 Problem Definition
18.3.2 Calculation and Discussions
18.4 Conclusions
References
19. Evaluation of Optimal Parameters to Enhance Worker’s Performance in an Automotive Industry
Rajat Yadav, Kuwar Mausam, Manish Saraswat and Vijay Kumar Sharma
19.1 Introduction
19.2 Methodology
19.3 Results and Discussion
19.4 Conclusions
References
20. Determining Key Influential Factors of Rural Tourism— An AHP Model
Puspalata Mahaptra, RamaKrishna Bandaru, Deepanjan Nanda and Sushanta Tripathy
20.1 Introduction
20.2 Rural Tourism
20.3 Literature Review
20.4 Objectives
20.5 Methodology
20.6 Analysis
20.7 Results and Discussion
20.8 Conclusions
20.9 Managerial Implications
References
21. Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment
Partha Pratim Bhattacharya, Kousik Bhattacharya, Sujit Kumar De, Prasun Kumar Nayak, Subhankar Joardar and Kushankur Das
21.1 Introduction
21.1.1 Overview
21.1.2 Motivation and Specific Study
21.2 Preliminaries
21.2.1 Pollution Function
21.2.2 Triangular Dense Fuzzy Set (TDFS)
21.3 Notations and Assumptions
21.3.1 Case Study
21.4 Formulation of the Mathematical Model
21.4.1 Crisp Mathematical Model
21.4.2 Formulation of Triangular Dense Fuzzy Mathematical Model
21.4.3 Defuzzification of Triangular Dense Fuzzy Model
21.5 Numerical Illustration
21.6 Sensitivity Analysis
21.7 Graphical Illustration
21.8 Merits and Demerits
21.9 Conclusion
Acknowledgement
Appendix
References
22. Common Yet Overlooked Aspects Accountable for Antiaging: An MCDM Approach
Rajnandini Saha, Satyabrata Aich, Hee-Cheol Kim and Sushanta Tripathy
22.1 Introduction
22.2 Literature Review
22.3 Analytic Hierarchy Process (AHP)
22.4 Result and Discussion
22.5 Conclusion
References
23. E-Waste Management Challenges in India: An AHP Approach
Amit Sutar, Apurv Singh, Deepak Singhal, Sushanta Tripathy and Bharat Chandra Routara
23.1 Introduction
23.2 Literature Review
23.3 Methodology
23.4 Results and Discussion
23.5 Conclusion
References
24. Application of k-Means Method for Finding Varying Groups of Primary Energy Household Emissions in the Indian States
Tanmay Belsare, Abhay Deshpande, Neha Sharma and Prithwis De
24.1 Introduction
24.2 Literature Review
24.3 Materials and Methods
24.3.1 Data Preparation
24.3.2 Methods and Approach
24.3.2.1 Cluster Analysis
24.3.2.2 Agglomerative Hierarchical Clustering
24.3.2.3 K-Means Clustering
24.4 Exploratory Data Analysis
24.5 Results and Discussion
24.6 Conclusion
References
25. Airwaves Detection and Elimination Using Fast Fourier Transform to Enhance Detection of Hydrocarbon
Garba Aliyu, Mathias M. Fonkam, Augustine S. Nsang, Muhammad Abdulkarim, Sandip Rashit and Yakub K. Saheed
25.1 Introduction
25.1.1 Airwaves
25.1.2 Fast Fourier Transform
25.2 Related Works
25.3 Theoretical Framework
25.4 Methodology
25.5 Results and Discussions
25.6 Conclusion
References
26. Design and Implementation of Control for Nonlinear Active Suspension System
Ravindra S. Rana and Dipak M. Adhyaru
26.1 Introduction
26.2 Mathematical Model of Quarter Car Suspension System
26.2.1 Mathematical Model
26.2.2 Linearization Method for Nonlinear System Model
26.2.3 Discussion of Result
26.3 Conclusion
References
27. A Study of Various Peak to Average Power Ratio (PAPR) Reduction Techniques for 5G Communication System (5G-CS)
Himanshu Kumar Sinha, Anand Kumar and Devasis Pradhan
27.1 Introduction
27.2 Literature Review
27.3 Overview of 5G Cellular System
27.4 PAPR
27.4.1 Continuous Time PAPR
27.4.2 Continuous Time PAPR
27.5 Factors on which PAPR Reduction Depends
27.6 PAPR Reduction Technique
27.6.1 Scrambling of Signals
27.6.2 Signal Distortion Technique
27.6.3 High Power Amplifier (HPA)
27.7 Limitation of OFDM
27.8 Universal Filter Multicarrier (UMFC) Emerging Technique to Reduce PAPR in 5G
27.8.1 Transmitter of UMFC
27.8.2 Receiver of UMFC
27.9 Comparison Between Various Techniques
27.10 Conclusion
References
28. Investigation of Rebound Suppression Phenomenon in an Electromagnetic V-Bending Test
Aman Sharma, Pradeep Kumar Singh, Manish Saraswat and Irfan Khan
28.1 Introduction
28.2 Investigation
28.2.1 Specimen for Tests
28.2.2 Design of Die and Tool
28.2.3 Configuration and Procedure
28.3 Mathematical Evaluation
28.3.1 Simulation Methodology
28.4 Modeling for Material
28.4.1 Suppressing Rebound Phenomenon
28.5 Conclusion
References
29. Quadratic Spline Function Companding Technique to Minimize Peak-to-Average Power Ratio in Orthogonal Frequency Division Multiplexing System
Lazar Z. Velimirovic
29.1 Introduction
29.2 OFDM System
29.2.1 PAPR of OFDM Signal
29.3 Companding Technique
29.3.1 Quadratic Spline Function Companding
29.4 Numerical Results and Discussion
29.5 Conclusion
Acknowledgment
References
30. A Novel MCGDM Approach for Supplier Selection in a Supply Chain Management
Bipradas Bairagi
30.1 Introduction
30.2 Proposed Algorithm
30.3 Illustrative Example
30.3.1 Problem Definition
30.3.2 Calculation and Discussions
30.4 Conclusions
References
Index

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