Real-world optimization problems are very difficult and have high degrees of uncertainty. Conventional optimization algorithms have some limitations (i.e., local solution attainment and/or divergence) in solving such problems. On the other hand, meta-heuristic algorithms prove to be competent in outperforming deterministic algorithms, especially when the complexity of the problem increases. Practitioners have utilized those unconventional algorithms for the past few decades. This paper presents an overview of the literature employing the Artificial Bee Colony (ABC) algorithm in their solution approach. The ABC algorithm is a recently introduced population-based meta-heuristic optimization technique inspired by the intelligent foraging behavior of honeybee swarms. Key features of the ABC algorithm, as well as its performance characteristics, are also discussed.