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Distributed Constrained Optimization for Second-Order Multiagent Systems via Event-Based Communication
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DOI：10.1109/TSMC.2024.3405453.png)
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Abstract
En 中文
This article studies the distributed constrained optimization problems for the discrete-time second-order multiagent systems (MASs), in which each agent privately owns local cost function and nonidentical convex set constraints. To solve this problem, a projection-based distributed event-triggered algorithm is developed via the constant step-sizes, which achieves an ergodic convergence rate O(1/k) for the general convex functions. By applying the event-triggered mechanism, the proposed algorithm can avoid unnecessary communication among the agents. Moreover, it is shown that the introduced event-triggered component does not sacrifice the convergence rate. Finally, a simulation example is carried out to demonstrate the theoretical results.
Keywords:
Optimization
Heuristic algorithms
Convergence
Distributed algorithms
Multi-agent systems
Machine learning algorithms
Costs
Distributed optimization
event-triggered communication
multiagent systems (MASs)
set constraints
second-order dynamics
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W