论文标题
最佳N网站选择问题的几何敏感群体感应算法
A Geometry-Sensitive Quorum Sensing Algorithm for the Best-of-N Site Selection Problem
论文作者
论文摘要
Temnothorax Albipennis蚂蚁的房屋狩猎行为使殖民地可以探索几种巢穴的选择并同意最好的巢穴。他们的行为是许多生物启发的群模型来解决相同问题的基础。但是，昆虫菌落和群文学中的许多现有现场选择模型仅在所有潜在的现场选择与群体的起始位置等距的设置上测试模型的准确性和决策时间。这些模型无法解决用不同几何形状的站点选择造成的地理挑战。例如，尽管实际的蚂蚁菌落能够始终如一地选择更高的质量，但在这种情况下，现有模型的进一步而不是较低的质量，现有模型的准确程度要少得多。如果现有模型在代理商的起始站点和更高质量的站点之间的路径上，那么现有的模型也更容易致力于低质量的网站。我们提出了一个用于网站选择问题的新模型，并通过模拟验证能够更好地应对这些地理挑战。我们的结果提供了对距离距离考虑距离的挑战类型的洞察力。我们的工作将使群体能够在更现实的情况下以许多不同的方式在环境中分发站点。
The house hunting behavior of the Temnothorax albipennis ant allows the colony to explore several nest choices and agree on the best one. Their behavior serves as the basis for many bio-inspired swarm models to solve the same problem. However, many of the existing site selection models in both insect colony and swarm literature test the model's accuracy and decision time only on setups where all potential site choices are equidistant from the swarm's starting location. These models do not account for the geographic challenges that result from site choices with different geometry. For example, although actual ant colonies are capable of consistently choosing a higher quality, further site instead of a lower quality, closer site, existing models are much less accurate in this scenario. Existing models are also more prone to committing to a low quality site if it is on the path between the agents' starting site and a higher quality site. We present a new model for the site selection problem and verify via simulation that is able to better handle these geographic challenges. Our results provide insight into the types of challenges site selection models face when distance is taken into account. Our work will allow swarms to be robust to more realistic situations where sites could be distributed in the environment in many different ways.
