标题： 基于关键性的动态拓扑优化以增强空中-海洋蜂群的弹性 显示英文标题

标题： Criticality-Based Dynamic Topology Optimization for Enhancing Aerial-Marine Swarm Resilience

摘要： 由于对抗环境中的定向通信中断和结构弱点，异构海空蜂群网络面临重大困难。 本文提出一个两步框架来增强网络的弹性。 具体而言，我们的框架结合基于关键性的节点优先级划分与多目标拓扑优化。 首先，我们设计了一个三层架构来表示蜂群网络的结构、通信和任务依赖关系。 然后，我们引入了SurBi-Ranking方法，该方法利用图卷积网络，以动态评估和实时排名节点和边的关键性。 接下来，我们应用NSGA-III算法优化网络拓扑，旨在平衡通信效率、全局连通性和任务成功率。 实验表明，与传统的K-Shell方法相比，我们的SurBi-Ranking方法更准确地识别关键节点和边，因为对这些组件的故意攻击会导致更严重的连通性退化。 此外，当在攻击下优先考虑SurBi-Ranked的关键组件时，我们的优化方法将自然连通性退化降低了约30%，实现了更高的任务成功率，并产生了更低的通信重新配置成本，确保在多阶段操作中持续的连通性和任务有效性。 显示英文摘要

摘要： Heterogeneous marine-aerial swarm networks encounter substantial difficulties due to targeted communication disruptions and structural weaknesses in adversarial environments. This paper proposes a two-step framework to strengthen the network's resilience. Specifically, our framework combines the node prioritization based on criticality with multi-objective topology optimization. First, we design a three-layer architecture to represent structural, communication, and task dependencies of the swarm networks. Then, we introduce the SurBi-Ranking method, which utilizes graph convolutional networks, to dynamically evaluate and rank the criticality of nodes and edges in real time. Next, we apply the NSGA-III algorithm to optimize the network topology, aiming to balance communication efficiency, global connectivity, and mission success rate. Experiments demonstrate that compared to traditional methods like K-Shell, our SurBi-Ranking method identifies critical nodes and edges with greater accuracy, as deliberate attacks on these components cause more significant connectivity degradation. Furthermore, our optimization approach, when prioritizing SurBi-Ranked critical components under attack, reduces the natural connectivity degradation by around 30%, achieves higher mission success rates, and incurs lower communication reconfiguration costs, ensuring sustained connectivity and mission effectiveness across multi-phase operations.