.png){kind=icon}
.png){kind=icon}
Return
Local consensus based multi-objective distributed optimization and its application
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sysconle.2025.106290.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In multi-agent networks, the objectives of the agents are often in conflict with each other. Most existing distributed optimization models optimize a single performance metric, which is usually a weighted sum of individual objectives. Thus it cannot fully reflect the trade-off among the objectives. In this paper, multiobjective distributed convex optimization problems with local optimal consensus in multi-agent systems are studied. A multi-objective distributed optimization algorithm based on the epsilon-constraint method is proposed and the convergence of the algorithm is proved. By changing epsilon value, the algorithm provides the decision maker with a set of representative efficient solutions to aid decision analysis. The effectiveness of the proposed method is verified through two numerical simulation examples. Furthermore, the proposed algorithm is applied to the classification problem with distributed data storage. The experimental results show that the proposed algorithm can effectively solve large-scale multi-objective optimization problems with distributed data storage.
Keywords:
Multi-objective distributed optimization
Local consensus constraint
-constraint method
Pareto frontier