A deep dive into the artificial bee colony algorithm: theory, improvements, and real-world applications

Optimization plays a vital role in tackling complex challenges across diverse fields such as engineering, computer science, data mining, and machine learning. Conventional optimization techniques often face difficulties when dealing with high-dimensional and nonlinear problems, which has led to the rise of metaheuristic algorithms as effective alternatives. The Artificial Bee Colony (ABC) algorithm, developed by Karaboga in 2005, is a nature-inspired optimization method modeled after the foraging behavior of honeybees. ABC has proven to be highly effective in solving nonlinear, multidimensional, and NP-hard optimization problems. This paper reviews the ABC algorithm, explores its various enhancements designed to improve convergence speed and the balance between exploration and exploitation, and examines its broad applications in areas like engineering, data mining, and medical diagnostics. The ongoing advancements in ABC, including its integration with other algorithms and adaptive parameter control, highlight its importance in contemporary optimization tasks.