A Review of The Artificial Bee Colony (ABC) Algorithm: Variants, Applications, And Performance
TL;DR
An integrated review of ABC is provided, covering its standard formulation, major variants, domain applications, benchmarking results, and emerging research directions, and highlights future opportunities in automated parameter tuning, multi‑objective optimization, and integration with deep learning frameworks.
Abstract
The Artificial Bee Colony (ABC) algorithm is a swarm intelligence technique inspired by the foraging behavior of honey bees. While widely studied, existing reviews often lack a comprehensive synthesis of algorithmic variants, comparative performance, and recent applications. This paper provides an integrated review of ABC, covering its standard formulation, major variants, domain applications, benchmarking results, and emerging research directions. A systematic survey of peer‑reviewed articles published between 2010 and 2025 was conducted using Scopus, Web of Science, IEEE Xplore, and ScienceDirect. Results show that hybrid ABC approaches (e.g., ABC combined with Differential Evolution) achieve up to 30% faster convergence and improved robustness under noisy conditions compared to the standard algorithm. Applications span engineering design, machine learning, telecommunications, cloud computing, and image processing, with reported gains in accuracy, efficiency, and scalability. The review identifies key limitations, including sensitivity to parameter settings and slower early convergence, and highlights future opportunities in automated parameter tuning, multi‑objective optimization, and integration with deep learning frameworks. This work contributes a holistic perspective by combining performance comparisons with application insights, offering a valuable resource for both researchers and practitioners.