Generalized Second-Order Neurodynamic Approach for Distributed Optimal Allocation
针对带等式与不等式资源交互及局部可行约束的分布式最优分配问题，提出了两种基于二阶动力学的广义神经动力学方法，并验证了其在切换通信拓扑下的收敛性。
In this article, based on the multiagent system with second-order dynamics, two neurodynamic approaches are proposed to solve the distributed optimal allocation problem (DOAP) with equality resource interaction, inequality resource interaction, and local feasible constraints.over the switching communication topologies. To address the equality and inequality resource interactions in a distributed way, the corresponding auxiliary variables are introduced to ensure the local estimations of Lagrangian multipliers reach consensus in a distributed manner. On this basis, a novel generalized second-order neurodynamic approach is presented to solve the nonsmooth DOAP, and the theoretical proof of convergence is provided. Furthermore, to prevent global information from being involved, another generalized second-order neurodynamic approach is designed and its effectiveness is also analyzed. Finally, a numerical example and an application of the maximum network utility problem are simulated to verify the correctness of the conclusions.