Abstract

Software-Defined Networking (SDN) has revolutionized network management by decoupling the control and data planes, enabling centralized, programmable, and dynamic routing. However, as network complexity and traffic demands grow, optimizing routing algorithms in SDN becomes a critical challenge. This research paper provides a comprehensive review of routing optimization techniques in SDN, analyzing traditional approaches, meta-heuristic algorithms, and machine learning-based strategies. We discuss the strengths and limitations of each method, highlight recent innovations such as hybrid meta-heuristics and reinforcement learning, and consider future directions for scalable, adaptive, and efficient SDN routing.