Optimization techniques in engineering : advances and applications / edited by Anita Khosla [and three others].

Format:

Book

Contributor:

Khosla, Anita, editor.

Series:

Sustainable Computing and Optimization Series

Language:

English

Subjects (All):

Engineering--Mathematical models.

Engineering.

Physical Description:

1 online resource (543 pages)

Place of Publication:

Hoboken, NJ : John Wiley & Sons, Inc., and Scrivener Publishing LLC, [2023]

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.

Contents:

Cover

Title Page

Copyright Page

Contents

Preface

Acknowledgment

Part 1: Soft Computing and Evolutionary-Based Optimization

Chapter 1 Improved Grey Wolf Optimizer with Levy Flight to Solve Dynamic Economic Dispatch Problem with Electric Vehicle Profiles

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

Chapter 2 Comparison of YOLO and Faster R-CNN on Garbage Detection

2.1 Introduction

2.2 Garbage Detection

2.2.1 Transfer Learning-Technique

2.2.2 Inception-Custom Model

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

Chapter 3 Smart Power Factor Correction and Energy Monitoring System

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

Chapter 4 ANN-Based Maximum Power Point Tracking Control Configured Boost Converter for Electric Vehicle Applications

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

Chapter 5 Single/Multijunction Solar Cell Model Incorporating Maximum Power Point Tracking Scheme Based on Fuzzy Logic Algorithm

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&amp

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&amp

5.4.4 Implementation of MPPT Scheme Based on FLA

5.5 Conclusion

Chapter 6 Particle Swarm Optimization: An Overview, Advancements and Hybridization

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

6.4.2 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

Chapter 7 Application of Genetic Algorithm in Sensor Networks and Smart Grid

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

Chapter 8 AI-Based Predictive Modeling of Delamination Factor for Carbon Fiber-Reinforced Polymer (CFRP) Drilling Process

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

Chapter 9 Performance Comparison of Differential Evolutionary Algorithm-Based Contour Detection to Monocular Depth Estimation for Elevation Classification in 2D Drone-Based Imagery

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

Chapter 10 Bioinspired MOPSO-Based Power Allocation for Energy Efficiency and Spectral Efficiency Trade-Off in Downlink NOMA

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

Chapter 11 Performances of Machine Learning Models and Featurization Techniques on Amazon Fine Food Reviews

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

Chapter 12 Optimization of Cutting Parameters for Turning by Using Genetic Algorithm

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

12.3.3 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

Chapter 13 Genetic Algorithm-Based Optimization for Speech Processing Applications

13.1 Introduction to GA

13.1.1 Enhanced 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

13.4 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

Chapter 14 Performance of P, PI, PID, and NARMA Controllers in the Load Frequency Control of a Single-Area Thermal Power Plant

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

Part 2: Decision Science and Simulation-Based Optimization

Chapter 15 Selection of Nonpowered Industrial Truck for Small Scale Manufacturing Industry Using Fuzzy VIKOR Method Under FMCDM Environment

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

Chapter 16 Slightly and Almost Neutrosophic gsα*-Continuous Function in Neutrosophic Topological Spaces

16.1 Introduction

16.2 Preliminaries

16.3 Slightly Neutrosophic gsα* - Continuous Function

16.4 Almost Neutrosophic gsα* - Continuous Function

16.5 Conclusion

Chapter 17 Identification and Prioritization of Risk Factors Affecting the Mental Health of Farmers

17.1 Introduction

17.2 Materials and Methods

17.2.1 ELECTRE Technique

17.3 Result and Discussion

17.4 Conclusion

Chapter 18 Multiple Objective and Subjective Criteria Evaluation Technique (MOSCET): An Application to Material Handling System Selection

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

Chapter 19 Evaluation of Optimal Parameters to Enhance Worker's Performance in an Automotive Industry

19.1 Introduction

19.2 Methodology

19.3 Results and Discussion

19.4 Conclusions

Chapter 20 Determining Key Influential Factors of Rural Tourism-An AHP Model

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

Chapter 21 Solution of a Pollution-Based Economic Order Quantity Model Under Triangular Dense Fuzzy Environment

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.

Notes:

Description based on print version record.

Includes bibliographical references and index.

ISBN:

9781119906391

1119906393

9781119906384

1119906385

OCLC:

1376934118