An Efficient Artificial Bee Colony Algorithm for Constrained Optimization Problems

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm which has shown a competitive performance with respect to other population-based algorithms. However, there are still some insufficiencies in ABC algorithm such as slow convergence and easily trapped in local optima. These drawbacks can be even more challenging when constraints are also involved. To address these issues an Efficient Constrained Artificial Bee Colony (eABC) algorithm is proposed where two new solution search equations are introduced respectively to employed bee and onlooker bee phases. In addition, smart flight operator is employed to be used in scout bee phase. This algorithm is tested on several constrained benchmark problems. The numerical results demonstrate that the eABC is competitive with other state of the art constrained ABC algorithms under consideration.