Distributed Event-Triggered Optimization Algorithm Design for MASs with Attacks on Communication Edges

Dandan Wang, Xiao Fang, Yan Wan, Guanghui Wen

Abstract: This paper aims to solve the distributed convex optimization problem for a class of multi-agent systems (MASs) with event-triggered communication and attacks on communication edges. Specifically, the local objective functions for agents are assumed to be strongly convex and continuously differentiable with Lipschitz continuous gradients. Unlike most of the MAS models considered in the existing literature, the attacks on communication edges among neighboring agents are considered and the effect of such attacks on optimal solution seeking is further analyzed. To solve such an optimization problem with relatively low communication cost among agents, a new kind of event-triggered optimization protocol is proposed for each agent. Sufficient yet efficient conditions are derived to ensure that the states of all agents exponentially converge to the optimal solution of the group objective function (i.e., the sum of all local objective functions). Furthermore, it is proved that the Zeno-behavior is excluded during the evolution of the MASs. Finally, some simulation studies are provided to illustrate the efficiency of the proposed event-triggered optimization protocol.