论文标题
使用循环随机优化的三维蜂群
Three-Dimensional Swarming Using Cyclic Stochastic Optimization
论文作者
论文摘要
在本文中，我们模拟了实施环状随机优化（CSO）算法的合作，移动传感剂的集合，以尝试调查和跟踪多个目标。在提出的CSO算法中，每个代理都使用其感知的测量结果，共享信息以及对他人未来动议的预测，以决定其下一个动作。选择此决定是为了最大程度地减少损失函数，该损失函数随着目标状态估计的不确定性降低而降低。每个代理只能使用此损失函数的嘈杂测量值，在这项研究中，每个代理都试图通过计算其随机梯度来最大程度地减少此函数。本文通过基于仿真的实验研究了CSO收敛在三个维度上的含义和适用性。
In this paper we simulate an ensemble of cooperating, mobile sensing agents that implement the cyclic stochastic optimization (CSO) algorithm in an attempt to survey and track multiple targets. In the CSO algorithm proposed, each agent uses its sensed measurements, its shared information, and its predictions of others' future motion to decide on its next action. This decision is selected to minimize a loss function that decreases as the uncertainty in the targets' state estimates decreases. Only noisy measurements of this loss function are available to each agent, and in this study, each agent attempts to minimize this function by calculating its stochastic gradient. This paper examines, via simulation-based experiments, the implications and applicability of CSO convergence in three dimensions.
