In this paper, a distributed neurodynamic approach is proposed for constrained convex optimization. The objective function is a sum of local convex subproblems, whereas the constraints of these subproblems are coupled. Each local objective function is minimized individually with the proposed neurodynamic optimization approach. Through information exchange between connected neighbors only, all nodes can reach consensus on the Lagrange multipliers of all global equality and inequality constraints, and the decision variables converge to the global optimum in a distributed manner. Simulation results of two power system cases are discussed to substantiate the effectiveness and characteristics of the proposed approach.

中文翻译：

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具有全局耦合约束的分布式优化的神经动力学方法


在本文中，提出了一种用于约束凸优化的分布式神经动力学方法。目标函数是局部凸子问题的总和，而这些子问题的约束是耦合的。使用所提出的神经动力学优化方法单独最小化每个局部目标函数。仅通过相连邻居之间的信息交换，所有节点就可以就所有全局平等和不平等约束的拉格朗日乘数达成共识，决策变量以分布式方式收敛到全局最优。讨论了两个电力系统案例的仿真结果，以证实所提出方法的有效性和特性。