Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

中文翻译：

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基于元启发式算法的软件定义网络动态路由优化

在大规模网络中，优化资源分配和路由以满足服务需求至关重要。软件定义网络（SDN）是一种将转发与控制解耦，通过编程实现动态管理和配置的新型网络范式，为部署智能控制算法（如深度强化学习算法）解决网络路由优化问题提供了可能。网络。这些基于智能的网络路由优化方案虽然能够捕捉网络状态特征，但容易陷入局部最优，导致收敛性能较差。为了解决这个问题，本文提出一种非洲秃鹰路由优化（AVRO）算法来实现SDN路由优化。 AVRO基于非洲秃鹰优化算法（AVOA），一种基于群体的元启发式智能优化算法，具有全局优化能力和收敛速度快的优势。首先，根据网络路由问题的特点，改进AVOA算法的群体初始化方法，增强算法对网络拓扑的感知能力。随后，我们添加了一个优化阶段来加强AVOA算法的开发并达到稳定的收敛效果。最后，我们对网络环境进行建模，定义网络优化目标，并与基线算法进行对比实验。实验结果表明，该路由算法具有更好的网络感知能力，较深度强化学习算法性能提升16.9%，较传统路由方案性能提升71.8%。