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June 11, 2025, 1:57 p.m.
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(Last updated: June 12, 2025, 6:40 a.m.)
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| ['Bansal S. Nature-inspired Metaheuristic Algorithms...Engineering Problems 2025.pdf'] | 0 bytes |
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| Uploaded by andryold1 | Size 48.8 MB | Health [ 38 /50 ] | Added 2025-06-11 |
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| Uploaded by indexFroggy | Size 10.6 MB | Health [ 23 /3 ] | Added 2024-11-15 |
NOTE
SOURCE: Bansal S. Nature-inspired Metaheuristic Algorithms...Engineering Problems 2025
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COVER
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MEDIAINFO
Textbook in PDF format This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities The purpose of this book is to provide various case studies where such nature-inspired techniques are used to solve optimization problems being faced by humans in their day-to-day life. The first chapter introduces metaheuristic algorithms and provides an overview of origins, important variants, and highlights the need and importance of metaheuristic optimization for providing solutions to real-world engineering problems. Chapter 2 provides a comprehensive overview of Quantum-Inspired Evolutionary Algorithms, detailing its foundational concepts, implementation techniques, and potential applications. Chapter 3 presents a detailed survey of various metaheuristic approaches and their application to the solution of a wide variety of single- and multi-objective engineering problems. Chapter 4 presents a hybrid system that combines lightweight encryption based on genetic algorithms with an innovative error control mechanism for more effectively addressing the challenges of securing data privacy and integrity during the migration of virtual machines. Chapter 5 explores the use of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) for hyperparameter tuning of Convolution Neural Network (CNN) models to enhance the accuracy and efficiency of image classification models. A comprehensive review of nature-inspired metaheuristic algorithms for Medical Image Analysis is presented in Chapter 6. The next two chapters discuss specific applications of metaheuristics in the medical domain: a PSO-based approach for protein structure prediction and refinement is presented in Chapter 7, while Chapter 8 describes quantum-inspired swarm optimization methods for medical image segmentation. Chapter 9 presents two hybrid approaches for community detection in social networks. Chapter 10 applies advanced soft computing techniques such as the adaptive neuro-fuzzy inference system (ANFIS) to effectively explore additive manufacturing parameters for improved tensile strength and functional electrode fabrication. Genetic algorithms are applied in Chapter 11 to solve a constrained optimal ordering and pricing problem in inventory management. The use of Gradient Boosting Decision Trees and Simulated Annealing for overspeed detection in an advanced IoT-based traffic management system is described in Chapter 12. Chapter 13 presents a novel approach that evaluates both the quality of solutions and the carbon footprint of computational processes for solving computationally challenging combinatorial problems, highlights how low-carbon heuristics can sometimes deliver near-optimal solutions with significantly reduced environmental impact, and advocates for the development of carefully hybridized energy-efficient metaheuristics for sustainable high-performance computing. The purpose of Chapter 14 is to discuss the importance of the PSO-SVR (support vector regression) algorithm in predicting the outcomes of real-world applications classified as healthcare, environmental, industrial, commercial, smart city, and other broad applications. The final chapter explores the fundamental concepts of Genetic Algorithms helpful in providing a comprehensive understanding of how GAs may be used to improve fraud detection systems. This book: Discusses load balancing and demand response using nature-inspired optimization techniques Presents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithms Covers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniques Explains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniques Illustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniques Nature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology
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