Distributed Optimization with Noisy Information Sharing
Distributed Optimization with Noisy Information Sharing
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DOI：
10.23919/ccc58697.2023.10240327
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发表时间：
2023-07
期刊：
影响因子：
--
通讯作者：
Yongqiang Wang
中科院分区：
文献类型：
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作者：
Yongqiang Wang
本文考虑多个智能体协同求解全局优化问题的分布式优化问题。提出了一种适用于一般有向网络拓扑结构的分布式梯度方法，该方法不需要参与主体维护或共享决策变量以外的任何额外变量。此外，通过在智能体之间的交互中加入衰减因素，该方法可以逐渐消除信息共享噪声的影响，并确保即使在持续的信息共享噪声存在的情况下，所有智能体也几乎必然收敛到相同的最优解。数值仿真结果证实了该方法的有效性。
This paper considers distributed optimization where multiple agents cooperatively solve a global optimization problem. This paper proposes a distributed gradient method that is applicable to general directed network topologies without requiring participating agents to maintain or share any extra variables besides the decision variable. Furthermore, by incorporating a decaying factor in inter-agent interactions, the proposed approach can gradually eliminate the influence of information-sharing noise and ensure the almost sure convergence of all agents to a same optimal solution even in the presence of persistent information-sharing noise. Numerical simulation results confirm the effectiveness of the proposed approach.