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Elsevier

Metaheuristic Optimization for Social Good

  • 1st Edition - April 1, 2027
  • Latest edition
  • Editors: Thompson Stephan, Seyedali Mirjalili, Vinaytosh Mishra
  • Language: English

Metaheuristic Optimization for Social Good presents a comprehensive guide that shows how powerful optimization algorithms—genetic algorithms, swarm intelligence, evolutionary… Read more

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Description

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.

Key features

  • Offers proven strategies for framing social welfare goals as optimization tasks.
  • Demonstrates how metaheuristics can be fine-tuned to handle ethical constraints and ensure fair resource allocation or unbiased decisions.
  • Provides real-world case studies and domain-specific best practices, reducing trial-and-error time for practitioners.

Readership

Computer Science researchers, artificial intelligence researchers, and researchers and practitioners working in the fields of data science, machine learning, and optimization. The primary audience also includes data analysts and software engineers, as well as researchers and professionals in the fields of social science, environmental science, engineers, and policy/decision makers

Table of contents

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

Product details

  • Edition: 1
  • Latest edition
  • Published: April 1, 2027
  • Language: English

About the editors

TS

Thompson Stephan

Dr. Thompson Stephan serves as an Assistant Professor at Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, United Arab Emirates. Dr. Thompson Stephan earned his PhD from Pondicherry University, India, in 2018, complemented by full-time research and industry expertise. Dr. Stephan has received prestigious accolades, including the Best Researcher Award in 2020 and the Protsahan Research Award in 2023, both from the IEEE Bangalore Section, India. His primary research focus is in artificial intelligence, with specialized expertise in advancing machine learning, data mining, and metaheuristic optimization, particularly in the context of high-dimensional data. With more than 80 Scopus-indexed publications, including 47 in SCI-indexed journals, Thompson’s work has garnered significant recognition. He actively contributes as a book editor and reviewer for esteemed international journals, with publications on leading platforms such as IEEE, Elsevier, Taylor & Francis, and Springer. Dr. Stephan has successfully four Scopus indexed edited book, two with Springer and two with Taylor & Francis, with another ongoing Springer book titled Hybrid Metaheuristic Optimization for Engineering Applications.

Affiliations and expertise
Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, United Arab Emirates

SM

Seyedali Mirjalili

Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence and optimization, recognized as the No. 1 AI researcher on Stanford University’s prestigious World’s Top Scientists list since 2023. He founded the Centre for Artificial Intelligence Research and Optimization in 2019 and serves as a Professor of AI at Torrens University Australia, with distinguished professorships in Hungary and the Czech Republic. With more than 600 research publications, 130,000 citations, and an H-index of 125, Prof. Mirjalili is among the top 1% of highly cited researchers worldwide. His contributions include developing AI algorithms widely applied in science and industry and delivering influential talks, including a TED Talk on AI's transformative potential. Prof. Mirjalili is a strong advocate for responsible and inclusive AI, and he has collaborated with industry and government on ethical AI tools. As a senior member of IEEE and an editor for leading AI journals, he significantly contributed to the advancements of fundamental and applied research in the field. Recognized as a top research leader by The Australian for five years, his insights have earned significant media attention, which showcases his influence as a global thought leader.
Affiliations and expertise
Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia, and EKIK, Obuda University, Budapest, Hungary

VM

Vinaytosh Mishra

Dr. Vinaytosh Mishra is an Associate Professor and Associate Dean at the Thumbay College of Management and AI in Healthcare, Gulf Medical University, UAE. He holds a PhD in Healthcare Supply Chain Management and has completed postdoctoral fellowships in AI in Healthcare and Ethical AI. With more than 19 years of experience spanning information technology, manufacturing, finance, healthcare, and education, he has held leadership roles including CEO of a multispecialty hospital. His research and teaching focuses on supply chain management, operations management, business analytics, AI-enabled decision support, digital transformation, optimization, simulation, Lean Six Sigma, and digital twins. He is an inventor on one Australian and two German patents in AI for healthcare and serves as a domain expert for AI implementation in healthcare at AHB.ai in Sharjah, UAE. He has designed and launched successful undergraduate and postgraduate programs in healthcare management and AI in healthcare.

Affiliations and expertise
Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, United Arab Emirates