Distributed Constraint Optimization with Flocking Behavior

This paper studies distributed optimization having flocking behavior and local constraint set. Multiagent systems with continuous-time and second-order dynamics are studied. Each agent has a local constraint set and a local objective function, which are known to only one agent. The objective is for...

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Bibliographic Details
Published in:Complexity (New York, N.Y.) N.Y.), 2018-01, Vol.2018 (2018), p.1-8
Main Authors: Yang, Zhengquan, Chen, Zengqiang, Zhang, Qing
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Collisions
Computer simulation
Connectivity
Convergence
Convex analysis
Mathematical optimization
Methods
Multiagent systems
Optimization
Phase transitions
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Summary:This paper studies distributed optimization having flocking behavior and local constraint set. Multiagent systems with continuous-time and second-order dynamics are studied. Each agent has a local constraint set and a local objective function, which are known to only one agent. The objective is for multiple agents to optimize a sum of the local functions with local interaction and information. First, a bounded potential function to construct the controller is given and a distributed optimization algorithm that makes a group of agents avoid collisions during the evolution is presented. Then, it is proved that all agents track the optimal velocity while avoiding collisions. The proof of the main result is divided into three steps: global set convergence, consensus analysis, and optimal set convergence. Finally, a simulation is included to illustrate the results.
ISSN:1076-2787
1099-0526
DOI:10.1155/2018/1579865