Description
The cyber–physical system (CPS) combines computation, communication, control, and physical components to achieve common goals with minimal human intervention. CPS components can operate on various temporal and spatial scales, having different independent decision-making behavioral modalities and cooperating synchronously to reach a common goal. CPS includes Smart Cities, Smart Grid, Industrial IoT, Cloud Computing, and "Smart" Anything (e.g., Transportation Systems, Cars, Manufacturing, Hospitals, Homes, Supply-Chain Management, Appliances, etc.). The CPS components are expected to grow exponentially and will generate zettabytes of data in the near future due to indispensable advancements in artificial intelligence (AI), machine learning (ML), and optimization fields. CPSs demand innovative solutions for various challenging problems, i.e., Resource Provisioning, Trust Management Systems, Data Security & Privacy, Computation Offloading to Cloud Computing, Secure Communication, Swarm Robotics, and Automation, Medical Image Analysis, Object detection, Remote Sensing Image Segmentation, Unmanned Aerial Vehicle (UAV) Path Planning, Water Distribution Networks, Network Intrusion Detection, Text Clustering, Wind Turbine Placement Problem, Optimal Feature Selection in High-Dimensional Datasets, Genome Sequence Assembly, Designing Optimal Neural Networks, and many others. These problems demand intelligent techniques that are different from the traditional algorithms and capable of handling CPS's current and upcoming challenges.
Nature-inspired optimization algorithms are a class of metaheuristic algorithms for optimization that can solve complex real-world problems that are not efficiently solvable by traditional methods or whose models are too complex for mathematical reasoning. Nature-inspired optimization algorithms are inspired by natural phenomena and are scalable, adaptable, and sustainable. They can be categorized into four categories, i.e., Evolution, Swarm, Physics-based, and Immune-based, depending on the principles of natural evolution, collaborative behavior, laws of physics, and natural immune system, respectively. Nature-inspired algorithms can successfully solve many complex real-world problems in acceptable computational time. Hence, Nature-inspired Optimization algorithms with their variants can handle the complex issues of CPS and act accordingly to create better values in our daily lives.
Objectives, Impact, and Value
This book is the result of research by the teaching staff and research scholars of leading universities and contains the results of many years of scientific experiments and theoretical conclusions. The research works were tested, presented, discussed, and formed the basis of the book as a result of the online forum "Nature-Inspired Optimization Algorithms for Cyber-Physical Systems".
This book explores nature-inspired optimization algorithms intended to boost the performance of cyber-physical systems. It discusses and presents the critical challenges, opportunities, applications, and advancement of Nature-Inspired Optimization Algorithms for the complex problems of cyber-physical systems.
Target Audience and Potential UsesThis book interests the teaching staff, research scholars, and graduate students of leading higher educational institutions in computer science, AI & Machine Learning, nature-inspired optimization algorithms, and Cyber-Physical systems. The book will also be helpful to IT professionals, data scientists, and AI researchers.