Design and Applications of Nature Inspired Optimization: Contribution of Women Leaders in the Field

Preface
Contents
Contributors
Chapter 1: An Overview of Swarm Intelligence-Based Algorithms
	1 Introduction
	2 Characteristics of SI-Based Algorithms
	3 Particle Swarm Optimization Algorithm
		Pseudocode of PSO
		Minimizing Rosenbrock Function Using PSO
	4 Artificial Bee Colony Algorithm
		Minimizing Schwefel´s Function Using Artificial Bee Colony Algorithm
	5 Comparative Analysis Using Fixed Iteration Test
	6 Conclusion
	References
Chapter 2: Particle Swarm Optimization and Its Applications in the Manufacturing Industry
	1 Introduction to Optimization
		Local and Global Optimal Solution
		Nature-Inspired Algorithms
	2 Particle Swarm Optimization
		How PSO Works
		Understanding PSO Parameters
		Binary Particle Swarm Optimization
		Research Developments in PSO
	3 Application of PSO Manufacturing Industry
	4 Conclusion
	References
Chapter 3: Role of Machine Learning in Bioprocess Engineering: Current Perspectives and Future Directions
	1 Introduction
	2 Approaches of Machine Learning in Bioprocess Engineering
	3 Why Machine Learning Strategies Are Needed in Bioprocess Engineering
	4 Applications of Machine Learning in Bioprocess Engineering (Case Studies)
		Approaches of Machine Learning in Biorefinery: A Case Study
		Approaches of Machine Learning in Monoclonal Antibody Production: A Case Study
		Approaches of Machine Learning for Antibiotic Production: A Case Study
		Machine Learning in Protein Engineering: A Case Study
	5 Current Challenges and Future Prospects
	6 Conclusion
	Glossary
	References
Chapter 4: Advanced Selection Operation for Differential Evolution Algorithm
	1 Introduction
	2 Basic Differential Evolution (DE)
	3 Proposed Modification
		Advance Selection Strategy
		Proposed DERLaS and MRLDEaS
	4 Experimental Settings
		Test Functions
		Performance Criteria
		Parameter Setting
	5 Result and Discussion
		Result on Benchmark Problems
		Result on Real-Life Application
		Convergence Graphs
	6 Conclusions
	References
Chapter 5: Profit Optimization of Two-Unit Briquetting System Using Grey Wolf Optimization Algorithm
	1 Introduction
	2 Introduction
	3 State Transition Diagram
	4 Transition Probabilities and Mean Sojourn Periods
	5 System Effectiveness Measures
		Mean Time to System Failure
		System Availability
		Busy Period
		Expected Visits by Repairmen
		Profit
	6 Grey Wolf Optimizer
	7 Graphical Results and Discussion
	8 Conclusions
	References
Chapter 6: Solving Portfolio Optimization Using Sine-Cosine Algorithm Embedded Mutation Operations
	1 Introduction
	2 Markowitz Model Based on Historical Stock Price Data
		Markowitz Mean-Variance Model
		Rate of Return
		Expected Return
		Variance
		Portfolio Formulation
		Statement of the Problem
	3 Problem Description
		Problem 1
		Problem 2
	4 Sine-Cosine Algorithm
		Mutation
		Power Mutation
		Polynomial Mutation
		Random Mutation
		Gaussian Mutation
		Cauchy Mutation
	5 Numerical Analysis of Results Obtained by the Proposed Version of SCA
		Problem 1
		Problem 2
	6 Result Analysis
	7 Conclusion
	References
Chapter 7: Detecting Group Shilling Profiles in Recommender Systems: A Hybrid Clustering and Grey Wolf Optimizer Technique
	1 Introduction
	2 Related Work and Motivation
	3 Shilling Attacks
	4 Grey Wolf Optimizer (GWO)
		Motivation
		Description and Algorithm
	5 GWODS
		Motivation
		Proposed Approach
	6 Experiments and Results
		Dataset and Experimental Setup
		Parameter Setting
		Evaluation Metrics
		Experiments and Results
			Comparison of Binary Operators
			Result Analysis
			Comparison of GWODS with State-of-the-Art Approaches
	7 Conclusion and Future Work
	References
Chapter 8: Single Image Reflection Removal Using Deep Learning
	1 Introduction
	2 Literature Survey
		Multi-image Methods
		Single Input Methods
		Traditional Approaches
		Learning-Based Approaches
	3 Proposed Method
		Training Dataset
		Model Description (Table 8.1)
		Loss Function
	4 Experiment and Results
		Training Details
		Experimental Set-Up
		Performance Evaluation Metrics
		Testing Dataset
	5 Conclusion and Future Work
	References
Chapter 9: Social Media Analysis: A Tool for Popularity Prediction Using Machine Learning Classifiers
	1 Introduction
	2 Related Works
	3 Proposed Methodology
		Problem Identification
		Data Gathering
		Data Filtering
		Fetching Features
		Classification Using ML Classifier
		Comparative Study of Different Models
		Implementing Tools
		Python
		Jupyter Notebook
		Statistical NLP, Machine Learning, and Deep Learning
		Application of NLP
	4 Result and Discussion
		Real-Time Applications
		Experimental Validation and Accuracy
	5 Conclusion and Future Scope
	6 Challenges and Limitations
	References
Appendix
	Benchmark Problems
	Real-Life Applications
		RF1: Parameter Estimation for Frequency-Modulated (FM) Sound Waves
		RF2: Optimal Thermohydraulic Performance of an Artificially Roughened Air Heater
		RF3: Spread-Spectrum Radar Polyphase Code Design
	References
Index