Open AccessDistributed multi‐agent optimisation via coordination with second‐order nearest neighbours
Author(s) -
Ren Xiaoxing,
Li Dewei,
Xi Yugeng,
Shao Haibin
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0708
Subject(s) - subgradient method , computer science , convergence (economics) , order (exchange) , multi agent system , k nearest neighbors algorithm , mathematical optimization , selection (genetic algorithm) , artificial intelligence , mathematics , machine learning , finance , economics , economic growth
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.