2015/05/14 by Mohsen Ghaffari, Ghaffari, Mohsen, Cameron Musco +5 · 2 citations
Computer Science · Engineering · #ANT #Ant colony #Ant colony optimization algorithms #Artificial intelligence #Biology #Computer science #Distributed Control Multi-Agent Systems #Distributed systems and fault tolerance #Ecology #Engineering #Nest (protein structural motif) #Optimization and Search Problems #Task (project management) #cs.DC
paper · pdf · doi:10.48550/arxiv.1505.03799
published in arXiv (Cornell University) (Cornell University) · To appear in PODC 2015
arxiv created 2015/05/14 · openalex publication_date 2015/05/14 · arxiv updated 2015/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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 Ω(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.