An event-triggered collaborative neurodynamic approach to distributed global optimization.

Abstract

In this paper, we propose an event-triggered collaborative neurodynamic approach to distributed global optimization in the presence of nonconvexity. We design a projection neural network group consisting of multiple projection neural networks coupled via a communication network. We prove the convergence of the projection neural network group to Karush-Kuhn-Tucker points of a given global optimization problem. To reduce communication bandwidth consumption, we adopt an event-triggered mechanism to liaise with other neural networks in the group with the Zeno behavior being precluded. We employ multiple projection neural network groups for scattered searches and re-initialize their states using a meta-heuristic rule in the collaborative neurodynamic optimization framework. In addition, we apply the collaborative neurodynamic approach for distributed optimal chiller loading in a heating, ventilation, and air conditioning system.