A self adaptive hybrid artificial bee colony algorithm for solving CEC 2013 real-parameter optimization problems

Artificial bee colony (ABC) algorithm is one of the most recently introduced swarm intelligence based algorithm which foraging the behavior of honey bee colonies. In order to improve the convergence performance and searching speed of finding best solution, self adaptive hybrid ABC (SAHABC) is proposed in this paper. For evaluating the performance of standard ABC and proposed SAHABC algorithms, we implemented experiments on CEC 2013 real-parameter single objective optimization problems testbed. SAHABC algorithm demonstrated competitive performance on the optimization problems with the dimension size of 10, 30, and 50 respectively.