Optimization of Clustering Problem Using Population Based Artificial Bee Colony Algorithm: A Review

The Artificial Bee Colony (ABC) algorithm is a population based meta-heuristic algorithm proposed by Karaboga in 2005 inspired by the intelligent foraging behaviour of honey bees. ABC as a powerful technique is easy to implement, has fewer control parameters, and could easily be integrated with other meta-heuristic algorithms due to which it continues to attract the interest of researchers from various fields around the world. Interestingly, ABC has been successfully applied to a wide variety of optimization problems. Clustering is an unsupervised classification mechanism where a data, usually multidimensional is classified into groups called clusters such that members of one group are similar according to a predefined criterion like minimizing square error criterion. The aim of this paper is to provide an extensive (not exhaustive) overview of modification to the original ABC and its application in solving Clustering problems with the expectation that it would serve as a reference material to both old and new, incoming researchers in this field. Keywords— Artificial Bee Colony Algorithms, Clustering, Nature-Inspired Meta-heuristics, Optimizations, Swarm Intelligence Algorithms.