Häftad, Engelska, 2027
1 824 kr
Kommande
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.
