【摘要】 在切换拓扑的多智能体系统中，针对高低密度信息减弱一致性的问题，提出了一种基于分层邻域选择算法(Layered Neighbor Selection, LNS)，该算法对智能体的邻域进行层次划分，从每层中选取具有代表性的邻居智能体进行通信、状态更新和状态演化，然后设计了层次调整策略和层次融合策略来加快收敛速度，最后设计了分层邻域选择一致性协议，且给出了层数对收敛的影响。现有的一致性协议，收敛效果受限于特定密度范围，受不同密度的影响较大，而本文提出的协议能适应不同密度范围，并在系统稳定的条件下提升收敛速度，且通过李雅普诺夫函数法证明了一致性协议的稳定性。最后通过仿真实验，并与几种一致性协议进行比较，验证了所设计的一致性协议能有效加快系统收敛速度。更多还原

【Abstract】 In the multi-agent systems with switching topology, to address the consistency problem weaken by the high and low density information, a Layered Neighbor Selection(LNS) algorithm is proposed. First, this algorithm divides the neighborhood of the agent into layers, and selects the representative neighbor agents from each layer for communication, state update and state evolution. Then, it designs a layer adjustment strategy and a layer fusion strategy to accelerate the convergence speed, Finally, it designs a layer neighborhood selection consistency protocol, and provides the influence of the number of layers on the convergence. The convergence effect of the traditional consistency protocol is limited to a specific density range and is greatly affected by different densities.Differently, the proposed protocol of this paper can adapt to different density ranges and improve the convergence speed under the condition of system stability. The stability of the consistency protocol is proved by the Lyapunov function method. The effectiveness of the proposed consistency protocol is verified by simulation in comparison with several consistency protocols.更多还原