A comprehensive survey on hybridization of artificial bee colony with particle swarm optimization algorithm and ABC applications to data clustering

ABC algorithm is bio-inspired algorithm which is derived from intelligent food search nature of the honey bee. But search equation of the ABC mostly depends on random search which is sufficient for exploration but insufficient for exploitation. Particle swarm optimization is an intelligent bio inspired algorithm having good global search property but poor exploitation property. Inspired from this to combine properties of both, In the current paper we provide review of different hybridization method (Component based, Multi stage, Cellular automata, Recombination, Chain) ABC with PSO to balance the exploration and exploitation processes, which results in improved convergence speed and avoidance of the local optima. The second portion of the paper presents a study on implementation of ABC to the data clustering.