Advances in Optimization Algorithms for Multidisciplinary Engineering ApplicationsКНИГИ » ПРОГРАММИНГ
Название: Advances in Optimization Algorithms for Multidisciplinary Engineering Applications Автор: Diego Oliva, Arturo Valdivia, Seyed Jalaleddin Mousavirad, Kanak Kalita Издательство: Springer Серия: Studies in Computational Intelligence Год: 2025 Страниц: 798 Язык: английский Формат: pdf (true) Размер: 48.9 MB
This book is an authoritative compilation of the latest advancements in optimization techniques. This book covers a wide array of methods ranging from classical to metaheuristic to AI-enhanced approaches.
The chapters are meticulously selected and organized in three sections—metaheuristics, Machine Learning and engineering applications. This allows for an in-depth exploration of diverse topics ranging from image processing to feature selection to data clustering, to practical applications like energy optimization, smart grids, healthcare diagnostics, etc. Each chapter delves into the specific algorithms and applications as well as provides ample theoretical insights.
Accordingly, this book is ideally suited for undergraduate and postgraduate students in fields such as science, engineering and computational mathematics. It is also an invaluable resource for courses on Artificial Intelligence, Computational Intelligence, etc. Researchers and professionals in evolutionary computation, Artificial Intelligence and engineering will find the material especially useful for advancing their work and exploring new frontiers in optimization.
Metaheuristic algorithms have gained popularity in addressing various optimization challenges in diverse fields such as digital image processing, energy, machine learning, robotics, and data analytics. Most metaheuristic methods are population-based, where a population of search agents (or individuals) explores different candidate solutions within a solution space. These optimization frameworks offer several advantages, including the interaction between individuals, which is crucial for effectively exploring the search space and overcoming local optima. Metaheuristic algorithms must balance exploring the search space for promising new solutions and exploiting regions containing high-quality solutions. Clustering and partitioning help divide the search space into smaller, more manageable regions, allowing effective exploration and intensive exploitation of promising regions. These techniques can be employed in a variety of metaheuristic algorithms. This chapter provides a comprehensive review of clustering and partitioning techniques within metaheuristic algorithms. Its purpose is to provide a detailed analysis of these techniques, identify their applications, advantages, and limitations, and facilitate their understanding and use in optimization problems.
I. Metaheuristics, Theory and Applications Multilevel Thresholding Color Image Segmentation Solved with Metaheuristics Optimization and Improve Image Contrast: A Comparative Study of Classical Metaheuristic Algorithms Grouping and Partitioning Methods in Metaheuristic Algorithms Image Contrast Enhancement: Harnessing Metaheuristics and the Gauss Error Function Diversity Measurement in Different PSO Variants Applied to Global Optimization and Classical Engineering Problems Pareto-Based Multiobjective Optimisation for JPEG Image Compression Evolutionary Self-Adjusting Masi Entropy Thresholding II. Machine Learning A Comparative Study of Bird-Based Metaphor Algorithms for Feature Selection Problems Metaheuristic Algorithms for Data Clustering in Multivariate Data Sets: A Comparative Analysis Innovative Machine Learning Techniques for Pedestrian Detection in Autonomous Vehicles Machine Learning-Enhanced Dynamic Routing for Internet of Things Energy Efficiency Adaptive Multigrid Long Short-Term Memory Algorithm for Improved Air Quality Forecasting A Hybrid Approach for Optic Disc Localization in Eye Fundus Images Classification of University Students Using Feature Selection and Wrapping Methods in a Pattern Recognition System Prediction of Heart Disease Using a Pattern Recognition Approach with Feature Selection and Naïve Bayesian Classifier Machine Learning and Data Analysis in the Prevention of Complications Derived from Diabetes Metaheuristic-Based Neuroevolution Framework for Improved Pneumonia Classification in X-ray Images PEL: Population-Enhanced Learning Classification for ECG Signal Analysis Enhancing Neural Network Generalisation with Improved Differential Evolution Evolutionary Algorithms in Code Smell Detection: A Feature Selection Approach for Software Engineering Exploratory Analysis of Machine Learning Methodologies Optimization for Facial Expression Recognition (FER) Prediction of Bitcoin Price Based on Optimized Support Vector Regression Using Modified Grey Wolf Optimizer Salp Swarm Algorithm Based Hyperparameter-Optimized Deep EfficientNet for COVID-19 Detection III. Engineering Applications Intelligent Model to Build an Smart Grid for Electrical Vehicles Integrated Distribution and Warehousing Optimization for Motorcycle Parts Using Dijkstra Algorithm, Container Packaging and Order Picking Numerical Approximation of the Maximum Absorption Capacity of the MEA-H2O Solution for a Post-combustion CO2 Capture Process Optimizing Smart Home Energy Analysis with Sailfish and Random Forest Algorithms Energy Consumption Optimization in Thread Machining by Various Hybrid Dragonfly Algorithms Neural Network Optimization of a Current Flow Meter with Applications in Hydroelectric Power Plants Analysis on the Performance of Evolutionary Strategies for Solving the Wind Farm Layout Optimization Problem Optimization of Radial Distribution Networks Through an Improved African Vulture Optimization Algorithm Smart Vehicle Charging with Variable Step Crow Search A Meta-Heuristic Approach to Improving Compressor Scheduling in Refrigerated Warehouses A Metaheuristic Task Scheduling of FOG Servers Using a Hybridization of Crow Search Algorithm with Non-Monopolize Search Mean-Shift Clustering for Failure Detection in Quadcopter Unmanned Aerial Vehicles
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