Buch, Englisch, 200 Seiten, Format (B × H): 191 mm x 235 mm
Buch, Englisch, 200 Seiten, Format (B × H): 191 mm x 235 mm
ISBN: 978-0-443-49064-4
Verlag: Elsevier Science
Metaheuristic Optimization for Social Good presents a comprehensive guide that shows how powerful optimization algorithms—genetic algorithms, swarm intelligence, evolutionary strategies, and other heuristics—can be tailored to address critical societal problems. Whereas existing references tend to focus on either the technical underpinnings of metaheuristics or broad “AI for social impact” overviews, this book demonstrates how metaheuristic approaches can be systematically and ethically applied to domains such as healthcare, urban planning, environmental stewardship, and policy-making. This handbook directly addresses these challenges by providing a structured approach to metaheuristics, presenting rigorous theoretical foundations, state-of-the-art hybrid techniques, and diverse real-world applications. The book consolidates research insights, methodological innovations, and practical use cases into a single, accessible volume. The book presents theoretical advances, hybrid model architectures, and practical applications, providing comprehensive coverage that combines metaheuristics with machine learning and data-driven simulation to enhance decision making for social good. The authors provide real-world case studies that cover the intersection of advanced optimization algorithms and multiple social domains in one volume, with technical depth in each topic. Readers learn how to ensure optimization results align with values such as fairness, equity, and sustainability, preparing readers to not only build effective solutions, but also ones that are socially acceptable and beneficial.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
1. Metaheuristic Algorithms for Societal Impact. An Overview
2. EQUI-MetaOpt. An Ethical Metaheuristic Framework for Equitable AI Optimization
3. Green-Energy MHO. A Metaheuristic Optimization Technique for Sustainable Power Distribution
4. Application of Grey Wolf Optimizer Algorithm for Load Frequency Control in Interconnected Power Systems
5. A Novel Metaheuristic Planner for Sustainable Traffic and Infrastructure
6. MediSchedA Metaheuristic Approach to Optimal Hospital Resource Scheduling
7. A Metaheuristic Technique for Fair Education Resource Allocation
8. A Data-Driven Metaheuristic Framework for Community Health Resource Optimization
9. Hybrid Metaheuristics for Potato Price Prediction with Time Series
10. Metaheuristic-Optimized Feature Selection and Machine Learning for Scalable Bearing Fault Diagnosis Across Diverse Operating Conditions
11. Hybrid-SPO/GWO-KNN. A Hybrid Binary Stochastic Paint Optimizer and Grey Wolf Optimizer with K-Nearest Neighbor Classifier for Feature Selection
12. Improved-GWO. An Improved Grey Wolf Optimizer for Wrapper Feature Selection
13. Sync-Async-GWO. Group-Based Synchronous-Asynchronous Grey Wolf Optimizer Enhanced by Diversity and Stagnation Control
14. Hybrid-B-GWO-SFS. Feature Selection Based on Hybrid Binary Grey Wolf Optimizer and Stochastic Fractal Search Algorithm
15. PSO-vs-ABC. Comparative Analysis of Particle Swarm Optimization and Artificial Bee Colony Algorithms for Non-Rigid Medical Image Registration
16. Meta-Heuristics + LLMs. Combined Metaheuristics with Large Language Models for Photonic Filter Design
17. MFDO-GWO. Advanced Hybrid Approach of Modified Falcon Optimization and Grey Wolf Optimization for Robust Engineering Applications
18. GWO-MGO. A Novel Hybrid Grey Wolf Optimizer with Mountain Gazelle Optimizer Algorithms for Solving Uncapacitated Facility Location Problems
19. Applying Metaheuristics for Risk-Aware Portfolio Optimization
20. Future Trends in Metaheuristic Optimization for Social Good
