HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization
This work addresses the need for efficient automated neural network design, offering a faster alternative for researchers and practitioners, though it appears incremental as it applies a known optimization method to a specific domain.
The authors tackled the problem of automating neural network design by proposing HiveNAS, a framework using Artificial Bee Colony optimization for Neural Architecture Search, which outperformed existing swarm intelligence-based NAS frameworks in significantly less time.
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.