Advances in Optimization Algorithms for Multidisciplinary Engineering Applications: From Classical Methods to AI-Enhanced Solutions

Preface
Contents
Metaheuristics, Theory and Applications
Multilevel Thresholding Color Image Segmentation Solved with Metaheuristics
	1 Introduction
	2 Multilevel Color Image Segmentation
	3 Pelican Optimization Algorithm
	4 Multilevel Color Image Segmentation Metaheuristic Implementation
	5 Experimental Conditions
		5.1 Experiments Setup and Benchmark Images for Testing
		5.2 Metrics for Segmentation
	6 Results and Discussion
	7 Medical Images Application
	8 Conclusion
	References
Optimization and Improve Image Contrast: A Comparative Study of Classical Metaheuristic Algorithms
	1 Introduction
	2 Metaheuristic Algorithms
		2.1 Differential Evolution (DE)
		2.2 Harmony Search (HS)
		2.3 Particle Swarm Optimization (PSO)
		2.4 Teaching Learning Based Optimization (TLBO)
		2.5 Artificial Bee Colony (ABC)
		2.6 Firefly Algorithm (FA)
		2.7 Invasive Weed Optimization (IWO)
	3 Transformation Functions and Objective Function as a Fundamental Basis to Improve Contrast in Images
	4 Experimental Results
		4.1 Experimental Configuration
		4.2 Results Performance
		4.3 Convergence Analysis
	5 Conclusions and Future Work
	References
Grouping and Partitioning Methods in Metaheuristic Algorithms
	1 Introduction
	2 Metaheuristic Algorithms for Optimization
		2.1 Evolutionary Computing Techniques
	3 Clustering Methods
		3.1 Hierarchical Clustering Methods
		3.2 Hierarchical Clustering Implementations with Metaheuristic Approach
		3.3 Novel Hierarchical Clustering Algorithm Based on Metaheuristic Algorithms for Software Modularization
		3.4 Improved Metaheuristic Approach for Hierarchical Clustering Implemented the Quartet Method
	4 Partitional Clustering Algorithms
		4.1 Squared Error Clustering Algorithm
		4.2 K-Means Algorithm
		4.3 Fuzzy C-Means Algorithm
		4.4 PAM Algorithm
		4.5 CLARA Algorithm
		4.6 CLARANS Algorithm
	5 Conclusions
	References
Image Contrast Enhancement: Harnessing Metaheuristics and the Gauss Error Function
	1 Introduction
	2 Literature Review
	3 General Background
		3.1 Contrast
		3.2 Quality Indicators in Digital Images
	4 Contrast Enhancement Using the Gauss Error Function
		4.1 Transformation Function
		4.2 Fitness Function
		4.3 Metaheuristics
		4.4 Contrast Enhancement Methodology
	5 Experimental Study
		5.1 Experimental Setup
		5.2 Performance Evaluation
	6 Analysis of Results
		6.1 Peak Signal-to-Noise Ratio
		6.2 Structural Similarity Index
		6.3 Relative Enhancement Contrast
	7 Conclusions and Future Work
	References
Diversity Measurement in Different PSO Variants Applied to Global Optimization and Classical Engineering Problems
	1 Introduction
	2 Particle Swarm Optimization Variants
		2.1 Particle Swarm Optimization
		2.2 Autonomous Groups Particles Swarm Optimization
		2.3 Biogeography-Based Learning Particle Swarm Optimization
		2.4 Comprehensive Learning Particle Swarm Optimizer
		2.5 An Improved PSO with Time-Varying Accelerator Coefficients
		2.6 Particle Swarm Optimization Gravitational Search Algorithm
		2.7 A Modified Particle Swarm Optimization with an Adaptive Acceleration Coefficients
		2.8 Particle Swarm Optimization Algorithm with Asymmetric Time-Varying Acceleration Coefficients
	3 Diversity in Metaheuristic Algorithms
		3.1 Dimension-Wise Diversity Measurement
		3.2 Average Population Diversity
		3.3 True Diversity
	4 Experiments and Results
		4.1 Experimental Results with CEC'2017 Benchmark
		4.2 Experimental Results with Applied Optimization Problems
	5 Conclusions and Future Work
	References
Pareto-Based Multiobjective Optimisation for JPEG Image Compression
	1 Introduction
	2 Preliminaries
		2.1 Multi-objective Optimisation
		2.2 JPEG Image Compression
	3 Proposed Approach
		3.1 Encoding Scheme
		3.2 Objective Function
		3.3 NSGAII-JPEG Algorithm
		3.4 NSGAIII-JPEG Algorithm
	4 Experimental Results
	5 Conclusion
	References
Evolutionary Self-Adjusting Masi Entropy Thresholding
	1 Introduction
	2 Background
		2.1 Differential Evolution
		2.2 Selection
		2.3 Masi Entropy
	3 DE-Based Self-Adjusting Masi Entropy
		3.1 Encoding Scheme
		3.2 Objective Function
		3.3 Evolutionary Operators
	4 Experiments
		4.1 Evaluation Criteria
		4.2 Results
	5 Conclusion
	References
Machine Learning
A Comparative Study of Bird-Based Metaphor Algorithms for Feature Selection Problems
	1 Introduction
	2 Materials and Methods
		2.1 Cuckoo Search
		2.2 Crow Search Algorithm
		2.3 Stain Bowerbird Optimization
		2.4 Emperor Penguin Optimizer
		2.5 Harris Hawks Optimization
	3 Experimental Details
	4 Results and Discussion
	5 Conclusions
	References
Metaheuristic Algorithms for Data Clustering in Multivariate Data Sets: A Comparative Analysis
	1 Introduction
	2 Metaheuristic Algorithms
	3 Clustering as an Objective Function of Metaheuristics
	4 Experimental Results
		4.1 Shapes Sets
		4.2 Synthetic Sets
		4.3 UCI Datasets
		4.4 Convergence Analysis
	5 Conclusions and Future Work
	References
Innovative Machine Learning Techniques for Pedestrian Detection in Autonomous Vehicles
	1 Introduction
	2 Methodology
		2.1 Algorithm Details
		2.2 Data Set
		2.3 Data Pre-processing
	3 Hybrid KNN Integrated Gaussian Naive Bayes Approach
		3.1 K-Nearest Neighbour (KNN)
		3.2 Gaussian Naive Bayes
	4 Result and Discussion
		4.1 Accuracy
		4.2 Precision
		4.3 Recall
		4.4 F1-Score
		4.5 Discussion
	5 Conclusion
	References
Machine Learning-Enhanced Dynamic Routing for Internet of Things Energy Efficiency
	1 Introduction
	2 Related Works
	3 Proposed Model
		3.1 Energy Model
		3.2 Network Model
		3.3 The Chain-Greedy Hierarchical Routing Protocol Based Fuzzy C-Means Algorithm (CGHRP-FCM)
		3.4 Pseudocode for CGHRP-FCM Protocol
	4 Results and Discussion
		4.1 Experimental Details
		4.2 Lifetime Network
		4.3 Residual Energy and Typical Node Energy Consumption
	5 Conclusion
	References
Adaptive Multigrid Long Short-Term Memory Algorithm for Improved Air Quality Forecasting
	1 Introduction
	2 Methodology
		2.1 Problem Statement
		2.2 Proposed Work
		2.3 Dataset
		2.4 Preprocessing
		2.5 Feature Extraction
		2.6 Adaptive Multigrid Long Short-Term Memory
	3 Results and Discussion
	4 Conclusions
	References
A Hybrid Approach for Optic Disc Localization in Eye Fundus Images
	1 Introduction
	2 Related Work
	3 Preliminary Concepts
		3.1 Optimized Top-Hat
		3.2 RUNge Kutta Optimizer (RUN)
		3.3 Harris Hawks Optimization (HHO)
		3.4 Minimum Cross-Entropy Thresholding (MCET)
	4 Proposed Method
		4.1 Color Channel Selection
		4.2 Transformation Function
		4.3 Fitness Function
		4.4 Multilevel Segmentation
		4.5 Centroid Calculation
	5 Experimental Framework
		5.1 Parameter Optimization Comparison
		5.2 Performance Metrics
		5.3 Dataset Description
	6 Results
		6.1 Optimization Analysis
		6.2 Segmentation Performance
		6.3 Centroid Calculation Performance
		6.4 Visual Analysis
	7 Conclusions
	References
Classification of University Students Using Feature Selection and Wrapping Methods in a Pattern Recognition System
	1 Introduction
		1.1 Contribution
	2 Background
	3 Theoretical Framework
		3.1 Pattern Recognition System
		3.2 Preprocessing Stage
		3.3 Feature Selection Stage
		3.4 Data Training
	4 Methods
	5 Results and Discussion
		5.1 Fisher Index
		5.2 Pearson’s Correlation
		5.3 Variance Threshold
		5.4 Forward Feature Selection
		5.5 Backward Feature Selection
		5.6 Exhaustive Selection
		5.7 Bayesian Theory Classifiers
		5.8 Naïve Bayes Classifier with Neyman Pearson
		5.9 Hidden Markov Models Classifier
	6 Conclusion
	References
Prediction of Heart Disease Using a Pattern Recognition Approach with Feature Selection and Naïve Bayesian Classifier
	1 Introduction
	2 Backgrounds
	3 Materials and Methods
		3.1 Preprocessing Stage
		3.2 Feature Selections
		3.3 Wrapper Methods
		3.4 Classifier Methods
	4 Results and Discussion
	5 Conclusion
	References
Machine Learning and Data Analysis in the Prevention of Complications Derived from Diabetes
	1 Introduction
	2 Related Works
	3 Impact of Diabetes in Mexico
	4 Diabetes Complications
		4.1 Hypoglycemia
		4.2 Hyperglycemia
		4.3 Macroangiopathy
		4.4 Ischemic Heart Disease
		4.5 Heart Failure
		4.6 Peripheral Artery Disease
		4.7 Ischemic Stroke
		4.8 Acute Kidney Failure
		4.9 Diabetic Retinopathy
		4.10 Diabetic Foot
	5 Machine Learning in Healthcare
		5.1 Advantages of Machine Learning in Healthcare
		5.2 The Attributes of Data Analytics in Healthcare
		5.3 Support Vector Machine
	6 SVM Model for the Prediction of Complications Derived from Diabetes
		6.1 Methodology and Data Processing Tools
		6.2 Machine Learning Model Results
	7 Conclusions
	References
Metaheuristic-Based Neuroevolution Framework for Improved Pneumonia Classification in X-ray Images
	1 Introduction
	2 Related Work
	3 Preliminary Concepts
		3.1 Neural Networks (NNs) Overview
		3.2 Transfer Learning
		3.3 Feature Extraction
		3.4 Support Vector Machine (SVM)
		3.5 Optimization Algorithms
	4 Proposed Framework
		4.1 Dataset Description
		4.2 CNN, SVM, and Optimizers Configuration
		4.3 Performance Metric
	5 Experimental Results and Discussion
	6 Conclusions
	References
PEL: Population-Enhanced Learning Classification for ECG Signal Analysis
	1 Introduction
	2 Background
		2.1 Neural Networks
		2.2 Gradient Descent with Momentum (GDM)
		2.3 Pattern Partitioning
		2.4 Differential Evolution (DE)
		2.5 Electrocardiogram (ECG)
	3 Methodology
		3.1 Dataset
		3.2 Preprocessing and Feature Extraction
		3.3 Enhancement of MLP Through Advanced DE Optimization
		3.4 Evaluation Metrics
	4 Experimental Results and Discussion
	5 Conclusion and Future Works
	References
Enhancing Neural Network Generalisation with Improved Differential Evolution
	1 Introduction
	2 Preliminaries
		2.1 Differential Evolution
		2.2 Neural Network Training
		2.3 Opposition-Based Learning
	3 Approches
		3.1 Encoding Scheme
		3.2 Cost Function
		3.3 DE-Based Training Algorithm
		3.4 Quasi-Opposition Infused Differential Evolution-Based Training (QODTA) Algorithm
		3.5 Centroid Quasi-Opposition Infused Differential Evolution-Based Training Algorithm
	4 Experimental Evaluation
	5 Conclusion
	References
Evolutionary Algorithms in Code Smell Detection: A Feature Selection Approach for Software Engineering
	1 Introduction
	2 Background
		2.1 Code Smells
		2.2 Feature Selection
		2.3 Wrapper-Based Methods
		2.4 Metaheuristic Algorithms
	3 Proposal
	4 Experiments
		4.1 Experimental Setup
		4.2 Dataset
		4.3 Classifier
		4.4 Rule Based Classification
	5 Discussion
		5.1 Results Rule-Based Approach
		5.2 Results on Feature Selection Using Metaheuristic Algorithms
	6 Conclusion
	References
Exploratory Analysis of Machine Learning Methodologies Optimization for Facial Expression Recognition (FER)
	1 Introduction
		1.1 Facial Action Coding System
		1.2 Arousal Valence 2D Model
		1.3 Challenges of Artificial Intelligence in Facial Expressions Recognition
	2 Facial Expression Recognition with Machine Learning Techniques
		2.1 Principal Component Analysis and K-Nearest Neighbors for FER
		2.2 Particle Swarm Optimization and Extreme Learning Machine for FER
		2.3 Bayesian Regression Network for FER
		2.4 Fuzzy Rough Feature Selection Inspired by Ant Colony Optimization and Fuzzy Rough Nearest Neighbor for FER
	3 Nonverbal Language Identification and Interpretation with Artificial Neural Network
		3.1 Deep Neural Network for FER
		3.2 Recurrent Neural Network for FER
		3.3 Multi-verse Optimizer Based on Whale Optimization Algorithm for FER
		3.4 Genetic Algorithms and Fuzzy Logic for FER
	4 Nonverbal Language Identification and Interpretation with Deep Learning
		4.1 Convolutional Neural Network for FER
		4.2 Deep Convolutional Neural Network for FER
		4.3 Generative Adversarial Network for FER
		4.4 Deep Belief Network Optimized and Spider Monkey Optimization for FER
	5 Discussion
	6 Conclusions and Future Scope
	References
Prediction of Bitcoin Price Based on Optimized Support Vector Regression Using Modified Grey Wolf Optimizer
	1 Introduction
	2 Related Works
	3 Methodology
		3.1 Grey Wolf Optimizer
		3.2 Support Vector Regression
	4 Experimental Results and Discussion
		4.1 Datasets
		4.2 Parameter Settings
		4.3 Results
	5 Conclusion and Future Work
	References
Salp Swarm Algorithm Based Hyperparameter-Optimized Deep EfficientNet for COVID-19 Detection
	1 Introduction
	2 Literature Review
	3 Background
		3.1 Salp Swarm Algorithm
		3.2 EfficientNet-B0
		3.3 Hyperparameter Optimized EfficientNet-B0 Using SSA
	4 Results and Discussion
		4.1 Datasets
		4.2 Performance Evaluation and Comparative Analysis
	5 Conclusion and Future Work
	References
Engineering Applications
Intelligent Model to Build an Smart Grid for Electrical Vehicles
	1 Introduction
	2 Requirements of a Smart Grid for a Electric Vehicle Fleet
	3 Use of Electric Vehicles in a Smart City
	4 Interconnection Between the Smart Grid and Electric Vehicles
	5 Benefits of Integrating Electric Vehicles in a Smart City with Smart Grid
		5.1 Objective Function Proposal
		5.2 Simulate Consumption Within the City
		5.3 Challenges and Obstacles for the Integration of Electric Vehicles in a Smart City with Smart Grid
		5.4 Case Studies and Examples of Successful Implementation
	6 Conclusions, Recommendations, and Future Research
	References
Integrated Distribution and Warehousing Optimization for Motorcycle Parts Using Dijkstra Algorithm, Container Packaging and Order Picking
	1 Introduction
	2 Dijkstra's Algorithm for Efficient Route Planning
	3 Methodology
	4 Data Collection
	5 Hub's Order Picking
	6 Distribution by Bin Packing for Spare Parts in Delivery Units
	7 Solving the Bin Packing
	8 Dijkstra Algorithm
	9 Conclusion and Future Research Directions
	References
Numerical Approximation of the Maximum Absorption Capacity of the MEA-H2O Solution for a Post-combustion CO2 Capture Process
	1 Introduction
	2 Proposed Methodology
		2.1 Description of the System
		2.2 Regression Techniques
		2.3 Topologies of the 18 Neural Networks
	3 Experimentation and Results
	4 Conclusions and Future Research
	References
Optimizing Smart Home Energy Analysis with Sailfish and Random Forest Algorithms
	1 Introduction
	2 Methodology
		2.1 Dataset
		2.2 Preprocessing Using Normalization
		2.3 Random Forest Algorithm
		2.4 Integration of Sailfish Optimization Algorithm with Random Forest
	3 Results and Discussion
		3.1 Energy Consumption
		3.2 Security
		3.3 Cost Savings
		3.4 Time Delay
	4 Conclusion
	References
Energy Consumption Optimization in Thread Machining by Various Hybrid Dragonfly Algorithms
	1 Introduction
	2 Methodology
		2.1 Dragonfly Algorithm (DA)
		2.2 Biogeography-Based Mexican Hat Wavelet Dragonfly Algorithm (BMDA)
		2.3 Chaotic Dragonfly Algorithm (CDA)
		2.4 Hybrid Dragonfly Algorithm with Differential Evolution (DADE)
		2.5 Hybridization of Dragonfly Algorithm and Artificial Bee Colony (HDA)
		2.6 Hybrid Nelder-Mead Algorithm and Dragonfly Algorithm (INMDA)
		2.7 Memory-Based Hybrid Dragonfly Algorithm (MHDA)
		2.8 Quantum-Behaved and Gaussian Mutational Dragonfly Algorithm (QGDA)
	3 Experimental Methods
		3.1 Calculation of Energy-Power Consumption
		3.2 Austempering Heat Treatment of Cast Iron Fittings
		3.3 Thread Machining of Cast Iron Fitting Samples
	4 Results and Discussion
		4.1 Energy-Power Consumption
		4.2 Regression Analysis
		4.3 Optimization Using Hybrid Dragonfly Algorithms
		4.4 Microstructural Evaluations
	5 Conclusions
	References
Neural Network Optimization of a Current Flow Meter with Applications in Hydroelectric Power Plants
	1 Introduction
		1.1 Characteristic Equations of the Propellers
	2 Methodology
		2.1 Instrumentation and Acquisition of the Angular Frequency Variable in (rpm)
		2.2 Signal Acquisition
		2.3 CAD Design
		2.4 Current Flow Meter Parts
		2.5 Manufacturing of Parts
		2.6 System Description
		2.7 Assembly and Adjustment in Wind Tunnel
		2.8 Experimental Test
	3 Results
		3.1 Obtaining the Constant ‘K’
		3.2 Data Analysis
		3.3 Comparison of Results Between Models
	4 Conclusions
	References
Analysis on the Performance of Evolutionary Strategies for Solving the Wind Farm Layout Optimization Problem
	1 Introduction
	2 The Wind Farm Layout Optimization Problem
		2.1 Wind Turbine Wake Model
		2.2 Optimization Formulation
	3 The Evolutionary Computation Strategies
		3.1 Differential Evolution
		3.2 Covariance Matrix Adaptation Evolution Strategy
		3.3 JADE
		3.4 Bernstein-Levy Differential Evolution Algorithm
		3.5 Bezier Search Differential Evolution Algorithm
	4 Computational Experiments
		4.1 Full Eclipse Results
		4.2 Partial Eclipse Results
		4.3 Statistical Analysis
	5 Discussion
	6 Conclusions
	References
Optimization of Radial Distribution Networks Through an Improved African Vulture Optimization Algorithm
	1 Introduction
	2 Mathematical Model for the OCP Problem
		2.1 Objective Functions
		2.2 Voltage Constraints
	3 The Optimization Process for the OCP
		3.1 The African Vulture Optimization Algorithm
		3.2 Improved Initialization Process
	4 Computational Experimentation
		4.1 IEEE's 33-Bus RDN System
		4.2 IEEE's 69-Bus RDN System
		4.3 Statistical Analysis
	5 Discussion
	6 Conclusions and Future Work
	References
Smart Vehicle Charging with Variable Step Crow Search
	1 Introduction
	2 Problem Statement
		2.1 Constraints
		2.2 Variables
		2.3 Fixed Parameters
	3 Meta Heuristic Algorithms
		3.1 Variable Step Crow Search Algorithm (CROW-VS)
		3.2 Accelerated Particle Swarm Optimization Algorithm (APSO)
		3.3 Invasive Weed Optimization Algorithm (IWO)
		3.4 Sine Cosine Algorithm (SCA)
		3.5 Genetic Algorithm (GA)
		3.6 Radial Movement Optimization Algorithm (RMO)
	4 Methodology
	5 Experiments
		5.1 The Result Set for 50 PHEVs
		5.2 The Result Set for 500 PHEVs
	6 Results and Discussion
	7 Conclusion
	References
A Meta-Heuristic Approach to Improving Compressor Scheduling in Refrigerated Warehouses
	1 Introduction
	2 Search Spaces
	3 Meta-Heuristic Algorithms
		3.1 IWO Invasive Weed Optimization
		3.2 GA Genetic Algorithm
		3.3 PSO Particle Swarm Optimization
		3.4 GWO Grey Wolf Optimizer
		3.5 DE Differential Evolution
	4 Refrigerated Room Model
		4.1 Heat Transfer and Thermal Energy
		4.2 Dynamic Model
	5 The Problem
		5.1 Problem Formulation
		5.2 Discrete Formulation
		5.3 Direct Continuous Formulation
		5.4 Indirect Continuous Formulation
	6 Experimental Study
		6.1 Common-Sense Schedule
		6.2 Binary Search Space Methods Discrete Formulation
		6.3 Continuous Search Space Methods with Standard Binarization Direct Continuous Formulation
		6.4 Continuous Methods for the Optimization of the Target Temperature Indirect Continuous Formulation
	7 Conclusions
	References
A Metaheuristic Task Scheduling of FOG Servers Using a Hybridization of Crow Search Algorithm with Non-Monopolize Search
	1 Introduction
	2 State of the Art
	3 Crow Search Algorithm Based in Non-monopolize Search
		3.1 Crow Search Algorithm (CSA)
		3.2 Non-monopolize Search Algorithm
		3.3 Crow Search Algorithm with Non-monopolize Search (CSA-NMS)
	4 CSA-NMS Applied to Tasks Scheduling Problems in Fog Computing
		4.1 Initialization
		4.2 Evaluation
		4.3 MakeSpan
		4.4 Energy
		4.5 Flow Time
		4.6 Fitness Function
	5 Experiments and Evaluations
		5.1 Wilcoxon Test Results
	6 Conclusions
	References
Mean-Shift Clustering for Failure Detection in Quadcopter Unmanned Aerial Vehicles
	1 Introduction
	2 Mean-Shift Algorithm
		2.1 Application of Mean-Shift in Clustering
	3 Experimental Scheme
		3.1 Hardware System
		3.2 Propeller Damage Scenarios
	4 Mean-Shift-Based UAV Malfunction System
		4.1 Vibration Measure System
		4.2 Metrics to Assess the Effectiveness
		4.3 Flight Failure Detection System
	5 Results
		5.1 Confusion Matrix Results
		5.2 Metrics Effectiveness Results
		5.3 Visual Subsystem Results
	6 Conclusions
	References