An Energy-Integrated Hybrid Cheetah Chase Framework for Adaptive Delay Optimization in Dynamic Network Routing

Abstract

Minimizing delay in selected paths is a critical challenge in modern network environments, particularly in large-scale and dynamic topologies. Traditional routing algorithms often struggle to provide optimal solutions for delay-sensitive applications due to their reliance on static metrics and limited adaptability. To address these limitations, this research develops a hybrid algorithm that integrates the Energy Function calculation of the Localizer SCAMP Joining Algorithm to optimize delays in selected paths. The proposed hybrid algorithm combines the strengths of bio-inspired and heuristic-based techniques to dynamically evaluate and minimize delays. By leveraging the Energy Function calculation, the algorithm assesses the cost-effectiveness of routing decisions based on energy consumption and path stability, ensuring efficient resource utilization and reduced transmission delays. The Localizer SCAMP framework enhances the algorithm's ability to adapt to changes in network topology, offering resilience against failures and improved convergence to delay-optimized paths. Extensive simulations and evaluations demonstrate that the hybrid algorithm significantly outperforms traditional approaches in delay reduction, particularly in networks with high variability and traffic dynamics. The results highlight the potential of this method in applications such as real-time communication systems, IoT networks, and vehicular ad hoc networks, where low-latency and reliable data transmission are paramount. This work provides a novel perspective on integrating energy-aware and adaptive strategies into network routing, advancing the state of the art in delay optimization for complex network systems.