论文标题
多目标多代理优化的算法
An Algorithm for Multi-Objective Multi-Agent Optimization
论文作者
论文摘要
在过去的二十年中，具有许多目标功能的多代理优化问题引起了很多兴趣。许多关于该主题的工作最小化目标函数的总和，该目标函数的总和隐含了有关问题制定的决定。实际上，它代表了多目标问题的特殊情况，其中所有目标均等优先级。据我们所知，应用于多代理系统的多目标优化在很大程度上尚未探索。因此，我们提出了一种分布式算法，该算法允许探索帕累托最佳解决方案，以实现目标函数的非合并加权总和。在我们考虑的问题中，每个代理都有一个目标函数，可以根据梯度方法最小化。代理通过与网络中的其他代理交换信息来更新其决策变量。信息交换是由每个代理商的各个权重编码其优先确定其他代理目标的程度的加权。本文提供了代理计算结果的收敛，性能界限和明确限制的证明。具有不同大小网络的仿真结果证明了所提出的方法的效率以及权重的选择如何影响代理的最终结果。
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 agents' 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.
