A Neurodynamic Approach to Distributed Optimization With Globally Coupled Constraints
提出一种分布式神经动力学方法，解决目标函数为局部凸子问题之和且约束耦合的优化问题，通过邻居间信息交换使所有节点对拉格朗日乘子达成共识，决策变量分布式收敛到全局最优，并用电力系统案例验证。
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.