Cat Swarm Optimization with Lévy Flight for Link Load Balancing in SDN

Efficient network communications with optimal network path selection play a key role in the modern world. Conventional path selection algorithms often face numerous challenges resulting from their limited scope of application. This research proposes a modified swarm intelligence approach, known as cat swarm optimization (CSO) with Lévy flight that is used for network link load balancing and routing optimization. CSO's quick convergence capabilities are suitable for rapid response applications; however, the approach is prone to getting stuck in local optima. Lévy flight enhances search efficiency, thus aiding in escaping local optima. CSO with Lévy flight (CSO-LF) outperforms original CSO and PSO algorithms in terms of solution quality and robustness across various benchmarks. The proposed method has been evaluated in software defined networks (SDN) with nine benchmark functions assessed. CSO-LF achieved the best scores in both the best and worst positions. When used in SDN for link load balancing, CSO-LF demonstrated lower latency and higher throughput than CSO, and lower latency and higher throughput than PSO in a fat tree topology.