A modified artificial bee colony algorithm for constrained optimization problems

Abstract

Artificial Bee Colony (ABC) algorithm is one of the prominent swarm intelligence algorithms which have been shown a competitive performance with respect to other population-based algorithms. However, this algorithm has poor exploitation ability. To address this issue, a modified Constrained Artificial Bee Colony (mcABC) algorithm is proposed where three new solution search equations are introduced respectively to employed bee, onlooker bee and scout bee phases. Furthermore, both chaotic search method and opposition-based learning mechanism are applied to initialize population in order to enhance the global convergence. This algorithm is tested on several constrained benchmark problems. The numerical results demonstrate that the mcABC is competitive with other state-of-the-art constrained ABC algorithm under consideration.