  The traditional Neural Network-development process requires substantial
expert knowledge and relies heavily on intuition and trial-and-error. Neural
Architecture Search (NAS) frameworks were introduced to robustly search for
network topologies, as well as facilitate the automated development of Neural
Networks. While some optimization approaches -- such as Genetic Algorithms --
have been extensively explored in the NAS context, other Metaheuristic
Optimization algorithms have not yet been investigated. In this study, we
evaluate the viability of Artificial Bee Colony optimization for Neural
Architecture Search. Our proposed framework, HiveNAS, outperforms existing
state-of-the-art Swarm Intelligence-based NAS frameworks in a fraction of the
time.
