Distributed Optimization for Second-Order Discrete-Time Multiagent Systems With Set Constraints

The optimization problem of second-order discrete-time multiagent systems with set constraints is studied in this article. In particular, the involved agents cooperatively search an optimal solution of a global objective function summed by multiple local ones within the intersection of multiple cons...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 34; no. 9; pp. 5629 - 5639
Main Authors: Zou, Yao, Xia, Kewei, Huang, Bomin, Meng, Ziyang
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.09.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Consensus
Constraints
Convergence
convex functions
Discrete time systems
Distributed algorithms
distributed optimization
Heuristic algorithms
Linear programming
Multi-agent systems
Multiagent systems
Objective function
Optimization
Parameters
Topology
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:The optimization problem of second-order discrete-time multiagent systems with set constraints is studied in this article. In particular, the involved agents cooperatively search an optimal solution of a global objective function summed by multiple local ones within the intersection of multiple constrained sets. We also consider that each pair of local objective function and constrained set is exclusively accessible to the respective agent, and each agent just interacts with its local neighbors. By borrowing from the consensus idea, a projection-based distributed optimization algorithm resorting to an auxiliary dynamics is first proposed without interacting the gradient information of local objective functions. Next, by considering the local objective functions being strongly convex, selection criteria of step size and algorithm parameter are built such that the unique solution to the concerned optimization problem is obtained. Moreover, by fixing a unit step size, it is also shown that the optimization result can be relaxed to the case with just convex local objective functions given a properly chosen algorithm parameter. Finally, practical and numerical examples are taken to verify the proposed optimization results.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3130173