树栖蚂蚁的巢选择是在空间约束下网络创造的一个紧急结果

IF 2.1 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Swarm Intelligence Pub Date : 2021-04-24 DOI:10.1007/s11721-021-00187-5
Joanna Chang, Scott Powell, Elva J. H. Robinson, Matina C. Donaldson-Matasci
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引用次数: 2

摘要

生物运输网络必须平衡相互竞争的功能优先级。用于生成此类网络的自组织机制启发了可扩展算法,以构建和维护低成本和高效的人为设计的运输网络。蚂蚁基于信息素的踪迹网络在这方面特别有价值。在这里,我们使用龟蚁作为我们的焦点系统:与通常用作自组织网络模型的蚂蚁物种相比,这些蚂蚁生活在空间受限的树栖环境中,筑巢选择和连接路径都是有限的。因此,他们必须解决一系列独特的挑战,这些挑战类似于受现有基础设施限制的人类运输工程师所面临的挑战。在这里,我们要问的是,海龟蚁群选择将哪些巢穴包括在一个网络中,可能会受到它们与其他巢穴建立联系的潜力的影响。在实验室实验中,我们发现不同种类的头螺(cephalalotes varans)和texanus头螺(cephalalotes texanus)的巢选择受到空间约束的影响,但以意想不到的方式。在一种空间构型下,蚁群优先占据连接较多的巢址;然而,在另一种空间构型下,这种偏好消失了。将这些实验结果与基于主体的模型进行比较,我们证明了巢连通性和巢选择之间的这种明显的特殊关系可以在没有巢偏好的情况下通过沿着受限路径的自我强化随机运动和巢中密度依赖聚集的组合出现。虽然这一机制并不总是导致重新建立低成本、高效率的运输网络,但它可能是扩大网络的有效方法,如果加上精简和改组的过程。
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Nest choice in arboreal ants is an emergent consequence of network creation under spatial constraints

Biological transportation networks must balance competing functional priorities. The self-organizing mechanisms used to generate such networks have inspired scalable algorithms to construct and maintain low-cost and efficient human-designed transport networks. The pheromone-based trail networks of ants have been especially valuable in this regard. Here, we use turtle ants as our focal system: In contrast to the ant species usually used as models for self-organized networks, these ants live in a spatially constrained arboreal environment where both nesting options and connecting pathways are limited. Thus, they must solve a distinct set of challenges which resemble those faced by human transport engineers constrained by existing infrastructure. Here, we ask how a turtle ant colony’s choice of which nests to include in a network may be influenced by their potential to create connections to other nests. In laboratory experiments with Cephalotes varians and Cephalotes texanus, we show that nest choice is influenced by spatial constraints, but in unexpected ways. Under one spatial configuration, colonies preferentially occupied more connected nest sites; however, under another spatial configuration, this preference disappeared. Comparing the results of these experiments to an agent-based model, we demonstrate that this apparently idiosyncratic relationship between nest connectivity and nest choice can emerge without nest preferences via a combination of self-reinforcing random movement along constrained pathways and density-dependent aggregation at nests. While this mechanism does not consistently lead to the de-novo construction of low-cost, efficient transport networks, it may be an effective way to expand a network, when coupled with processes of pruning and restructuring.

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来源期刊
Swarm Intelligence
Swarm Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
CiteScore
5.70
自引率
11.50%
发文量
11
审稿时长
>12 weeks
期刊介绍: Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research and developments in the multidisciplinary field of swarm intelligence. The journal publishes original research articles and occasional review articles on theoretical, experimental and/or practical aspects of swarm intelligence. All articles are published both in print and in electronic form. There are no page charges for publication. Swarm Intelligence is published quarterly. The field of swarm intelligence deals with systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. It is a fast-growing field that encompasses the efforts of researchers in multiple disciplines, ranging from ethology and social science to operations research and computer engineering. Swarm Intelligence will report on advances in the understanding and utilization of swarm intelligence systems, that is, systems that are based on the principles of swarm intelligence. The following subjects are of particular interest to the journal: • modeling and analysis of collective biological systems such as social insect colonies, flocking vertebrates, and human crowds as well as any other swarm intelligence systems; • application of biological swarm intelligence models to real-world problems such as distributed computing, data clustering, graph partitioning, optimization and decision making; • theoretical and empirical research in ant colony optimization, particle swarm optimization, swarm robotics, and other swarm intelligence algorithms.
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Resilient swarm behaviors via online evolution and behavior fusion Decentralized traffic management of autonomous drones Non-uniform magnetic fields for collective behavior of self-assembled magnetic pillars The viability of domain constrained coalition formation for robotic collectives Imprecise evidence in social learning
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