Clustering Approach Based On Von Neumann Topology Artificial Bee Colony Algorithm

Article Bee Colony (ABC) is one of the most recently introduced algorithms based on the intelligent foraging behavior of a honey bee swarm. This paper proposes a new variant of the ABC algorithm based on Von Neumann topology structure, namely Von Neumann Neighborhood Article Bee Colony (VABC). VABC significantly improves the original ABC in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. The most popular technique for clustering is k-means algorithm. However, the k-means algorithm highly depends on the initial state and converges to local optimum olution. In this work, VABC algorithm is tested on a set of widely-used benchmark functions and is used for solve data clustering on several benchmark data sets. The performance of VABC algorithm is compared with ABC and Particle Swarm Optimization (PSO) algorithms. The simulation results show that the proposed VABC outperforms the other two algorithms in terms of accuracy, robustness, and convergence speed