Event-Triggered Discrete-Time ZNN Algorithm for Distributed Optimization with Time-Varying Objective Functions

Abstract

This paper investigates discrete-time distributed optimization for multi-agent systems (MASs) with time-varying objective functions. A novel fully distributed event-triggered discrete-time zeroing neural network (ET-DTZNN) algorithm is proposed to address the discrete-time distributed time-varying optimization (DTDTVO) problem without relying on periodic communication. Each agent determines the optimal solutions by relying solely on local information, such as its own objective function. Moreover, information is exchanged with neighboring agents exclusively when event-triggered conditions are satisfied, significantly reducing communication consumption. The ET-DTZNN algorithm is derived by discretizing a proposed event-triggered continuous-time ZNN (ET-CTZNN) model via the Euler formula. The ET-CTZNN model addresses the time-varying optimization problem in a semi-centralized framework under continuous-time dynamics. Theoretical analyses rigorously establish the convergence of both the ET-CTZNN model and the ET-DTZNN algorithm. Simulation results highlight the algorithm’s effectiveness, precision, and superior communication efficiency compared with traditional periodic communication-based approaches.