This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.

This book aims to gather high-quality research papers on developing theories, frameworks, architectures, and algorithms for solving complex challenges in smart healthcare applications for real industry use. It explores the recent theoretical and practical applications of metaheuristics and optimization in various smart healthcare contexts. The book also discusses the capability of optimization techniques to obtain optimal parameters in ML and DL technologies. It provides an open platform for academics and engineers to share their unique ideas and investigate the potential convergence of existing systems and advanced metaheuristic algorithms. The book''s outcome will enable decision-makers and practitioners to select suitable optimization approaches for scheduling patients in crowded environments with minimized human errors.

The healthcare system aims to improve the lives of disabled, elderly, sick individuals, and children. IoT-based systems simplify decision-making and task automation, offering an automated foundation. Nature-inspired metaheuristics and mining algorithms are crucial for healthcare applications, reducing costs, increasing efficiency, enabling accurate data analysis, and enhancing patient care. Metaheuristics improve algorithm performance and address challenges in data mining and ML, making them essential in healthcare research. Real-time IoT-based healthcare systems can be modeled using an IoT-based metaheuristic approach to generate optimal solutions.

Metaheuristics are powerful technologies for optimization problems in healthcare systems. They balance exact methods, which guarantee optimal solutions but require significant computational resources, with fast but low-quality greedy methods. Metaheuristic algorithms find better solutions while minimizing computational time. The scientific community is increasingly interested in metaheuristics, incorporating techniques from AI, operations research, and soft computing. New metaheuristicsoffer efficient ways to address optimization problems and tackle unsolved challenges. They can be parameterized to control performance and adjust the trade-off between solution quality and resource utilization. Metaheuristics manage the trade-off between performance and solution quality, making them highly applicable to real-time applications with pragmatic objectives.

This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.

This book aims to gather high-quality research papers on developing theories, frameworks, architectures, and algorithms for solving complex challenges in smart healthcare applications for real industry use. It explores the recent theoretical and practical applications of metaheuristics and optimization in various smart healthcare contexts. The book also discusses the capability of optimization techniques to obtain optimal parameters in ML and DL technologies. It provides an open platform for academics and engineers to share their unique ideas and investigate the potential convergence of existing systems and advanced metaheuristic algorithms. The book''s outcome will enable decision-makers and practitioners to select suitable optimization approaches for scheduling patients in crowded environments with minimized human errors.

The healthcare system aims to improve the lives of disabled, elderly, sick individuals, and children. IoT-based systems simplify decision-making and task automation, offering an automated foundation. Nature-inspired metaheuristics and mining algorithms are crucial for healthcare applications, reducing costs, increasing efficiency, enabling accurate data analysis, and enhancing patient care. Metaheuristics improve algorithm performance and address challenges in data mining and ML, making them essential in healthcare research. Real-time IoT-based healthcare systems can be modeled using an IoT-based metaheuristic approach to generate optimal solutions.

Metaheuristics are powerful technologies for optimization problems in healthcare systems. They balance exact methods, which guarantee optimal solutions but require significant computational resources, with fast but low-quality greedy methods. Metaheuristic algorithms find better solutions while minimizing computational time. The scientific community is increasingly interested in metaheuristics, incorporating techniques from AI, operations research, and soft computing. New metaheuristicsoffer efficient ways to address optimization problems and tackle unsolved challenges. They can be parameterized to control performance and adjust the trade-off between solution quality and resource utilization. Metaheuristics manage the trade-off between performance and solution quality, making them highly applicable to real-time applications with pragmatic objectives.