A Collaborative Neurodynamic Approach to Distributed Global Optimization
Zicong Xia, Yang Liu, Jun Wang
Abstract
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.