Distributed Event-Triggered Algorithms for Multi-Objective Resource Allocation Problem

This paper considers a multi-objective resource allocation problem for a multi-agent network where each agent has multiple local objective functions. The goal is to obtain the Pareto optimum by exchanging information between agents. To this end, first, we introduce the weighted L_p preference index to reformulate this problem into a single-objective resource allocation problem, in which the weighting factor of each objective depends on its relative importance. Moreover, in order to reduce the communication burden, we propose distributed event-triggered algorithms to solve the reformulated problem. When local objective functions are strongly convex and have Lipschitz gradients, we prove that the proposed algorithms are free of Zeno behavior and achieve exponential convergence. Finally, we demonstrate the effectiveness of the proposed algorithms by a microgrid network example.