An improved bee colony optimization algorithm with an application to document clustering

The bee colony optimization (BCO) algorithm is proved to be one of the fast, robust and efficient global search heuristics in tackling different practical problems. Considering BCO algorithm in this paper, we utilize it for the data clustering, a fundamental problem that frequently arises in many applications. However, we discovered some obstacles in directly applying the ancient BCO to address the clustering problem and managed to change some basic behaviors of this swarm algorithm. In particular, we present an improved bee colony optimization algorithm, dubbed IBCO, by introducing cloning and fairness concepts into the BCO algorithm and make it more efficient for data clustering. These features give BCO very powerful and balanced exploration and exploitation capabilities to effectively guide the search process toward the proximity of the high quality solutions. In particular, the cloning feature allows it to take advantage of experiences gained from previous generations when generating new solutions. The problem of getting stuck in local optima still laid bare in the proposed improved version. As a result, to overcome the shortage of this swarm algorithm in searching locally, we hybridize it with the k-means algorithm to take advantage of fine tuning power of the widely used k-means algorithm which demonstrates good result in local searches. We propose four different hybridized algorithms based on IBCO and k-means algorithms and investigate the clustering results and convergence behavior of them. We empirically demonstrate that our hybrid algorithms alleviate the problem of sticking in a local solution even for large and high dimensional data sets such as document clustering. The results show that proposed algorithms are robust enough to be used in many applications compared to k-means and other recently proposed evolutionary based clustering algorithms including genetic, particle swarm optimization, ant colony, and bee based algorithms. (C) 2015 Published by Elsevier B.V.