A Fixed-Time Distributed Optimization Algorithm Based on Dynamic Event-Triggered Strategy

This paper investigates the fixed-time distributed optimization problem of first-order multi-agent systems with strongly convex local cost functions and consensus constraints. To address this problem, this paper designs a two-stage fixed-time optimization algorithm based on dynamic event-triggered strategy. Firstly, the first-stage algorithm is designed based on the gradient information of the local cost function, ensuring that the agent states converge to the local optimal values within a fixed time. Secondly, the second-stage algorithm is designed based on the dynamic event-triggered strategy and the Hessian matrix of the local cost function, achieving the convergence of agent states from the local optimal values to the global optimal value within a fixed time. The algorithm proposed in this paper solves the problem of excessive consumption of communication resources in the continuous communication optimization algorithm. It enables all agents to reach the global optimal state in fixed time, and the upper bound of the setting time does not depend on the initial values of the system. The numerical simulations validate the effectiveness of the algorithm.