An Algorithm for Multi-Objective Multi-Agent Optimization

被引:0
|
作者
Blondin, Maude J. [1 ]
Hale, Matthew [1 ]
机构
[1] Univ Florida, Dept Mech & Aerosp Engn, Gainesville, FL 32611 USA
来源
2020 AMERICAN CONTROL CONFERENCE (ACC) | 2020年
关键词
SUBGRADIENT METHODS; CONSENSUS; CONVERGENCE;
D O I
10.23919/acc45564.2020.9148017
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Multi-agent optimization problems with many objective functions have drawn much interest over the past two decades. Many works on the subject minimize the sum of objective functions, which implicitly carries a decision about the problem formulation. Indeed, it represents a special case of a multi-objective problem, in which all objectives are prioritized equally. To the best of our knowledge, multi-objective optimization applied to multi-agent systems remains largely unexplored. Therefore, we propose a distributed algorithm that allows the exploration of Pareto optimal solutions for the non-homogeneously weighted sum of objective functions. In the problems we consider, each agent has one objective function to minimize based on a gradient method. Agents update their decision variables by exchanging information with other agents in the network. Information exchanges are weighted by each agent's individual weights that encode the extent to which they prioritize other agents' objectives. This paper provides a proof of convergence, performance bounds, and explicit limits for the results of their computations. Simulation results with different sizes of networks demonstrate the efficiency of the proposed approach and how the choice of weights impacts the agents' final result.
引用
收藏
页码:1489 / 1494
页数:6
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