Information Security Center, State Key Laboratory of Networking and Switching Technology, andPotsdam Institute for Climate Impact Research, D14473 Potsdam, Germany; and.
Information Security Center, State Key Laboratory of Networking and Switching Technology, and
Proc Natl Acad Sci U S A. 2014 Jun 10;111(23):8392-7. doi: 10.1073/pnas.1407083111. Epub 2014 May 27.
The study of the foraging behavior of group animals (especially ants) is of practical ecological importance, but it also contributes to the development of widely applicable optimization problem-solving techniques. Biologists have discovered that single ants exhibit low-dimensional deterministic-chaotic activities. However, the influences of the nest, ants' physical abilities, and ants' knowledge (or experience) on foraging behavior have received relatively little attention in studies of the collective behavior of ants. This paper provides new insights into basic mechanisms of effective foraging for social insects or group animals that have a home. We propose that the whole foraging process of ants is controlled by three successive strategies: hunting, homing, and path building. A mathematical model is developed to study this complex scheme. We show that the transition from chaotic to periodic regimes observed in our model results from an optimization scheme for group animals with a home. According to our investigation, the behavior of such insects is not represented by random but rather deterministic walks (as generated by deterministic dynamical systems, e.g., by maps) in a random environment: the animals use their intelligence and experience to guide them. The more knowledge an ant has, the higher its foraging efficiency is. When young insects join the collective to forage with old and middle-aged ants, it benefits the whole colony in the long run. The resulting strategy can even be optimal.
群体动物（尤其是蚂蚁）觅食行为的研究具有实际的生态重要性，但它也有助于开发广泛适用的优化问题解决技术。生物学家已经发现，单个蚂蚁表现出低维确定性混沌活动。然而，在蚂蚁集体行为的研究中，巢、蚂蚁的物理能力和蚂蚁的知识（或经验）对觅食行为的影响相对较少受到关注。本文为具有家园的群居昆虫或动物的有效觅食的基本机制提供了新的见解。我们提出，蚂蚁的整个觅食过程由三个连续的策略控制：捕猎、归巢和路径构建。我们建立了一个数学模型来研究这个复杂的方案。我们表明，我们模型中观察到的从混沌到周期状态的转变是由具有家园的群居动物的优化方案引起的。根据我们的调查，这种昆虫的行为不是由随机的而是由确定性的行走（例如由确定性动力系统生成，例如通过映射）来表示，即随机环境中的行为：动物利用它们的智慧和经验来引导自己。蚂蚁拥有的知识越多，它的觅食效率就越高。当年轻的昆虫与年老和中年的蚂蚁一起加入集体觅食时，从长远来看，这对整个殖民地都有利。所产生的策略甚至可以是最优的。
