Distributed House-Hunting in Ant Colonies

TL;DR

This paper models the ant colony house-hunting process as a distributed computing problem, establishing theoretical bounds and presenting algorithms that mimic natural ant behavior for efficient nest selection.

Contribution

It introduces a formal model for ant house-hunting, proves a lower bound on consensus time, and provides two algorithms with different trade-offs for the process.

Findings

Lower bound of Ω(log n) on time to reach consensus

An optimal O(log n) time algorithm with unnatural features

A simple, natural O(k log n) algorithm

Abstract

We introduce the study of the ant colony house-hunting problem from a distributed computing perspective. When an ant colony's nest becomes unsuitable due to size constraints or damage, the colony must relocate to a new nest. The task of identifying and evaluating the quality of potential new nests is distributed among all ants. The ants must additionally reach consensus on a final nest choice and the full colony must be transported to this single new nest. Our goal is to use tools and techniques from distributed computing theory in order to gain insight into the house-hunting process. We develop a formal model for the house-hunting problem inspired by the behavior of the Temnothorax genus of ants. We then show a \Omega(log n) lower bound on the time for all n ants to agree on one of k candidate nests. We also present two algorithms that solve the house-hunting problem in our model. The first algorithm solves the problem in optimal O(log n) time but exhibits some features not characteristic of natural ant behavior. The second algorithm runs in O(k log n) time and uses an extremely simple and natural rule for each ant to decide on the new nest.