Optimizing Delay in Network Routing: A Hybrid Algorithm with Energy Function Integration in the Cheetah Chase Framework

Minimizing delay in selected paths is a critical challenge in modern network environments, particularly in largescale
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.