Software-Defined Networking (SDN) represents a significant paradigm shift within the realm of computer networks, transitioning from traditional architectures to a three-layered structure. As SDN continues to revolutionize network management and resource allocation, the need for efficient and adaptable optimization techniques becomes increasingly critical. This study delves into the comparative analysis of two approaches, the Tailor Aquila Optimizer (TAO) and the Aquila Wild Geese Optimizer (AWGO), designed to compute optimal paths within SDN environments. The objective of this study is to evaluate and compare the performance of TAO and AWGO in addressing routing optimization challenges inherent in SDN, including minimizing delay and maximizing throughput. Results demonstrate that sensitive traffic experiences lower delay values compared to best-effort traffic across both TAO and AWGO methodologies. Additionally, empirical findings reveal that AWGO exhibits superior performance in path computation for both traffic types compared to TAO. Through this comparative investigation, valuable insights are gleaned into the efficacy of TAO and AWGO in addressing route optimization. The findings of this study offer valuable insights into the strengths and weaknesses of TAO and AWGO in SDN environments, providing guidance for network engineers and researchers in selecting the most suitable optimization approach for specific SDN routing objectives.