Toward Intelligent Routing in Sdn: a Comparative Review of Bio-Inspired Optimization Techniques

Abstract

Bio-inspired routing algorithms have gained significant attention as effective approaches for solving complex optimization problems in modern communication networks. Drawing inspiration from collective behaviors in nature, these methods have been widely applied in decentralized environments such as wireless sensor and mobile ad hoc networks. However, their adoption within Software-Defined Networks (SDN) remains relatively limited. This paper provides a systematic and comparative review of bio-inspired routing algorithms with an emphasis on their applicability in SDN. A novel taxonomy is proposed to classify these algorithms according to their behavioral characteristics and their suitability for centralized control. In addition, a unified evaluation framework is introduced to enable consistent comparison among key approaches, including Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, and Grey Wolf Optimization, based on performance criteria such as convergence, scalability, and quality of service. The study also includes an SDN-oriented analysis, examining the impact of centralized control on the behavior and performance of these algorithms. Finally, the paper outlines key research challenges, highlighting the importance of real-time optimization, hybrid methodologies, and integration with intelligent control mechanisms.