Adaptive Computational Intelligence Algorithms for Efficient Resource Management in Smart Systems

The rapid evolution of smart systems, including Internet of Things (IoT) devices, smart grids, and autonomous vehicles, has led to the need for efficient resource management to optimize performance, reduce energy consumption, and enhance system reliability. This paper presents adaptive computational intelligence (CI) algorithms as an effective solution for addressing the dynamic challenges in resource management for smart systems. Specifically, we explore the application of techniques such as fuzzy logic, genetic algorithms, particle swarm optimization, and neural networks to adaptively manage resources like energy, bandwidth, processing power, and storage in real-time. These CI algorithms offer robust decision-making capabilities, enabling smart systems to efficiently allocate resources based on environmental changes, system demands, and user preferences. The paper discusses the integration of these algorithms with real-time data acquisition systems, providing a framework for adaptive and scalable resource management. Additionally, we evaluate the performance of these algorithms in various smart environments, highlighting their ability to optimize system efficiency, reduce operational costs, and improve the overall user experience. The proposed approach demonstrates significant improvements over traditional resource management techniques, making it a promising solution for next-generation smart systems.