Distributed multi‐agent optimisation via coordination with second‐order nearest neighbours

Abstract

The authors examine the distributed multi-agent optimisation problem allowing each agent to explicitly utilise the information of its first-order and second-order nearest neighbours for cooperative decision-making. Specifically, at each time instance, each agent in the network employs the states of its second-order nearest neighbours at the last time instance, which can be transmitted from its first-order nearest neighbours in practice. Under the proposed framework, they propose a distributed subgradient algorithm asymptotically leading all agents to an agreement on the optimal solution of the multi-agent optimisation problem under an appropriate selection of stepsize. Furthermore, they show that the proposed algorithm outperforms the existing algorithms built on the assumption that only the states of first-order neighbours are available in terms of the convergence rate.