Artificial bee colony (ABC) algorithm has been introduced for solving numerical optimization problems, inspired collective behavior of honey bee colonies. ABC algorithm has three phases named as employed bee, onlooker bee and scout bee. In the model of ABC, only one design parameter of the optimization problem is updated by the artificial bees at the ABC phases by using interaction in the bees. This updating has caused the slow convergence to global or near global optimum for the algorithm. In order to accelerate convergence of the method, using a control parameter (modification rate-MR) has been proposed for ABC but this approach is based on updating more design parameters than one. In this study, we added directional information to ABC algorithms, instead of updating more design parameters than one. The performance of proposed approach was examined on well-known nine numerical benchmark functions and obtained results are compared with basic ABC and ABCs with MR. The experimental results show that the proposed approach is very effective method for solving numeric benchmark functions and successful in terms of solution quality, robustness and convergence to global optimum. 
A directed artificial bee colony algorithm