The network performance and the unmanned aerial vehicle (UAV) number are important objectives when UAVs are placed as communication relays to enhance the multi-agent information exchange. The problem is a non-deterministic polynomial hard (NP-hard) multi-objective optimization problem, instead of generating a Pareto solution, this work focuses on considering both objectives at the same level so as to achieve a balanced solution between them. Based on the property that agents connected to the same UAV are a cluster, two clustering-based algorithms, M-K-means (MKM) and modified fast search and find density of peaks (MFSFDP) methods, are first proposed. Since the former algorithm requires too much computational time and the latter one requires too many relays, an algorithm for the balanced network performance and relay number (BPN) is proposed by discretizing the area to avoid missing the optimal relay positions and defining a new local density function to reflect the network performance metric. Simulation results demonstrate that the proposed algorithms are feasible and effective. Comparisons between these algorithms show that the BPN algorithm uses fewer relay UAVs than the MFSFDP and classic set-covering based algorithm, and its computational time is far less than the MKM algorithm.

中文翻译：

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在两层多代理系统中放置无人机作为通信中继：基于聚类的方法

当无人机作为通信中继以增强多智能体信息交换时，网络性能和无人机（UAV）数量是重要的目标。该问题是一个非确定性多项式困难 (NP-hard) 多目标优化问题，而不是生成帕累托解决方案，这项工作侧重于在同一级别考虑两个目标，以实现它们之间的平衡解决方案。基于连接到同一无人机的代理是一个集群的特性，首先提出了两种基于聚类的算法，MK-means (MKM) 和改进的快速搜索和查找峰值密度 (MFSFDP) 方法。由于前一种算法需要太多的计算时间，而后一种算法需要太多的中继，通过将区域离散化以避免错过最佳中继位置并定义新的局部密度函数来反映网络性能指标，提出了一种平衡网络性能和中继数（BPN）的算法。仿真结果表明所提出的算法是可行和有效的。这些算法之间的比较表明，BPN算法比MFSFDP和经典的基于集合覆盖的算法使用更少的中继无人机，其计算时间远小于MKM算法。