A distributed optimization algorithm for multi-agent systems with limited communication

In distributed optimization of multi-agent systems, agents usually cooperate to minimize a global function which is a sum of local objective functions. In this paper, distributed optimization of multi-agent systems is studied in which the global function is in a general form. A communication rule and a distributed algorithm are designed to solve the cooperative optimization problem of multi-agent systems with limited communication which is embodied by an undirected graph. The algorithm is designed according to the proposed communication rule which can share global information indirectly by transmitting information among the agents even though communications among the agents are limited, and is carried out through the true values of agents' states instead of the estimated ones. A simulation example of distributed optimization of a four-agent system with limited communication is presented to show the effectiveness of the proposed algorithm.