A consensus algorithm based on multi-agent system with state noise and gradient disturbance for distributed convex optimization

Almost all systems are inevitably subject to various uncertainties or disturbances from the external environment in practical applications. Taking these factors into consideration, in this paper a distributed algorithm with state noise and gradient disturbance is proposed for solving distributed opt...

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Veröffentlicht in:Neurocomputing (Amsterdam) Jg. 519; S. 148 - 157
Hauptverfasser: Meng, Xiwang, Liu, Qingshan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 28.01.2023
Schlagworte:
Distributed optimization
External disturbance
Gradient tracking algorithm
Multi-agent system
Projection error
Weight-balanced graph
Weight-balanced graph
Distributed optimization
Projection error
Multi-agent system
External disturbance
Gradient tracking algorithm
ISSN:0925-2312, 1872-8286
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Zusammenfassung:Almost all systems are inevitably subject to various uncertainties or disturbances from the external environment in practical applications. Taking these factors into consideration, in this paper a distributed algorithm with state noise and gradient disturbance is proposed for solving distributed optimization problem with closed convex set constraint based on multi-agent system under weight-balanced graph. Moreover, based on the gradient tracking and projection methods, the proposed distributed algorithm with gradient tracking can improve the convergence rate by introducing a projection error term and an auxiliary parameter. In contrast to some existing constrained distributed gradient algorithms, the proposed one can make the convergence faster and enhance the performance of convergence. The proposed algorithm is illustrated with two simulation examples to show its effectiveness and robustness.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2022.11.051