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An Upper and Lower Bound for the Convergence Time of House-Hunting in Temnothorax Ant Colonies.

Emily ZhangJiajia ZhaoNancy Lynch
Published in: Journal of computational biology : a journal of computational molecular cell biology (2022)
We study the problem of house-hunting in ant colonies, where ants reach consensus on a new nest and relocate their colony to that nest, from a distributed computing perspective. We propose a house-hunting algorithm that is biologically inspired by Temnothorax ants. Each ant is modeled as a probabilistic agent with limited power, and there is no central control governing the ants. We show an Ω ( log n ) lower bound on the running time of our proposed house-hunting algorithm, where n is the number of ants. Furthermore, we show a matching upper bound of expected O ( log n ) rounds for environments with only one candidate nest for the ants to move to. Our work provides insights into the house-hunting process, giving a perspective on how environmental factors such as nest quality or a quorum rule can affect the emigration process.
Keyphrases
  • machine learning
  • neural network
  • high intensity
  • clinical practice
  • quality improvement