Cyber-physical systems (CPS) integrate computation, communication, control, and physical elements to achieve shared goals with minimal human intervention, encompassing smart technologies such as cities, cloud computing, and smart grids. As CPS components expand, generating vast amounts of data, they face challenges in areas like resource management, security, computation offloading, and automation, demanding advanced techniques beyond traditional algorithms. Nature-inspired optimization algorithms, drawing on natural phenomena, offer scalable and adaptable solutions for these complex issues, making them essential for addressing CPS challenges efficiently and enhancing their role in our daily lives. Nature-Inspired Optimization Algorithms for Cyber-Physical Systems provides relevant theoretical frameworks and the latest empirical research findings in the area. It explores the nature-inspired optimization algorithms intended to boost the performance of CPS. Covering topics such as ant colony optimization, data analysis, and smart cities, this book is an excellent resource for teaching staff, researchers, academicians, graduate and postgraduate students, and more.
