A Collaborative Neurodynamic Approach to Distributed Global Optimization
提出一种协同神经动力学方法，通过连接多个投影神经网络组成循环神经网络组，并引入元启发式规则重新初始化神经元状态，以解决非凸函数的分布式全局优化问题。
In this article, we present a collaborative neurodynamic approach to distributed optimization with nonconvex functions. We develop a recurrent neural network (RNN) group by connecting individual projection neural networks through a communication network. We prove the convergence of the RNN group to the local optimal solutions of a given distributed optimization problem. We propose a collaborative neurodynamic optimization system with multiple RNN groups for scattered searches and a metaheuristic rule for reinitializing the neuronal states upon their local convergence. We elaborate on three numerical examples to demonstrate the efficacy of the proposed approach to distributed global optimization in the presence of nonconvexity.