论文标题
通过集体学习优化觅食者的殖民地的位置
Optimizing the location of the colony of foragers with Collective Learning
论文作者
论文摘要
动物团体一生相互合作,以更好地理解周围的环境。在这里,我们尝试使用连续的随机步行,整个出生过程,生殖和死亡的整个过程可能影响搜索过程。我们试图模拟一个生态系统,在该生态系统中,后期生化的觅食者离开殖民地,发现目标在哪里,而其他人则留在基地。实际上,一群觅食者搜索了他们从那里访问粮食供应目标的地点。特别是,我们探讨了一种假设的情况,在这种情况下,搬迁到新立场取决于该物种的一致性水平以及由于该协议水平而导致的额外等待时间。在此背景中,详细的数值结果表明,在合适的协议级别的合适范围内,可以在最佳平均时间搜索最佳位置。我们还显示,在给定的一致性水平上,最佳平均时间随死亡与出生比的线性增加。
Animal groups collaborate with one another throughout their lives to better comprehend their surroundings. Here, we try to model, using continuous random walks, how the entire process of birth, reproduction, and death might impact the searching process. We attempt to simulate an ecosystem where the post-reproductive foragers leave their colonies to discover where the targets are while others stay and breed at the base. Actually, a group of foragers searches for a location from where they access the targets for food supply. Particularly, we have explored a hypothetical situation in which the relocation to the new position depends on the agreement level of the species as well as an additional waiting time due to this agreement level. In this backdrop, detailed numerical results reveal that searching for an optimal position at an optimal mean time can be captured for a suitable range of the agreement level. We have also shown, for a given agreement level, the optimal mean time linearly increases with the Death-to-Birth ratio.