"Real-World Optimization Using the Cheetah Chase Algorithm Models, Performance, and Applications"

The Cheetah Chase Algorithm (CCA), inspired by predator-prey dynamics, offers a bio-inspired solution to a wide range of optimization challenges across diverse domains. Its adaptive decision-making, energy efficiency, and ability to identify cost-effective paths make it highly versatile for several real-world applications. In network routing, the CCA enhances path selection by minimizing routing costs and convergence delays, making it suitable for dynamic and large-scale network environments. In healthcare applications, the algorithm optimizes data transmission in sensor networks for patient monitoring, ensuring timely and accurate delivery of critical health data. In robotics and autonomous vehicles, the CCA supports efficient path planning and obstacle avoidance, enhancing navigation in uncertain and dynamic environments. The algorithm also shows promise in supply chain logistics, where it can streamline delivery routes and resource allocation, reducing costs and improving operational efficiency. Its application in Vehicular Ad Hoc Networks (VANETs) enables reliable and low-latency communication among vehicles, enhancing traffic management and road safety. Additionally, in VLSI (Very-Large-Scale Integration) design, the CCA aids in optimizing interconnect routing and reducing signal delays, contributing to more efficient chip layouts. The adaptability and resource-efficient nature of the Cheetah Chase Algorithm make it a powerful tool for solving complex optimization problems across multiple industries. This research highlights the broad applicability of the CCA, setting the stage for further exploration of its capabilities in advancing modern technological systems.