A new hybrid artificial bee colony algorithm for global optimization

To further improve the performance of artificial bee colony algorithm (ABC), a new hybrid ABC (HABC) for global optimization is proposed via exploring six initialization methods. Furthermore, to balance the exploration and exploitation abilities, a new search mechanism is also developed. The algorithms are applied to 27 benchmark functions with various dimensions to verify its performance. Numerical results demonstrate that the proposed algorithms outperforms the ABC in global optimization problems, especially the HABC algorithm with random initialization and HABCO algorithm with orthogonal initialization.