Abstract: Artificial Bee Colony (ABC) algorithm is a relatively new Swarm Intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation which is good at exploration but poor at exploitation. This drawback can be even more significant when constraints are also involved. To address this issue, an Enhanced ABC algorithm (EABC) is proposed for constrained optimization problems where two new solution search equations are introduced for employed bee and onlooker bee phases, respectively. In addition, both Chaotic Search Method and opposition-based learning mechanism are employed to be used in population initialization in order to enhance the global convergence when producing initial population. This algorithm is tested on several benchmark functions where the numerical results demonstrate that the EABC is competitive with state of the art constrained ABC algorithm.
Soudeh Babaeizadeh and Rohanin Ahmad, 2015. Enhanced Artificial Bee Colony Algorithm for Constrained Optimization Problems. Research Journal of Applied Sciences, 10: 241-250.