Metaheuristic Optimization for Social Good

Thompson Stephan · Seyedali Mirjalili · Vinaytosh Mishra

Apr 2027 · Morgan Kaufmann

Ebook

203

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This book will become available on April 1, 2027. You will not be charged until it is released.

About this ebook

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. - 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.

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About the author

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.

Dr. Seyedali Mirjalili is a Professor and globally renowned leader in artificial intelligence andoptimization, 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 andOptimization 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 researchpublications, 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 TheAustralian for five years, his insights have earned significant media attention, which showcaseshis influence as a global thought leader.

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.

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