PSO ingrained Artificial Bee Colony algorithm for solving continuous optimization problems

Artificial Bee Colony (ABC) algorithm is one approach that has been used to find an optimal solution in numerical optimization problems. This algorithm is inspired by the foraging behavior of honey bees when seeking a quality food source. ABC can sometimes trap into local optimum and also slow to converge. In ABC, the employed bees and onlooker bees carry out exploration and exploitation use the same equation. Obviously, the performance of ABC greatly depends on single equation. To enrich the searching behavior and to avoid being trapped into local optimum, PSO is incorporated into the ABC. In order to improve the algorithm performance, we present a modified method for solution update of the employed as well as onlooker bees in this paper. The proposed variants are termed as EABC-PSO and OABC-PSO. To show the performance of our proposed variants, experiments are carried out on a set of well-known benchmark problems. Simulation results and comparisons with the standard ABC and PSO show that the proposed variants can effectively enhance the searching efficiency and greatly improve the searching quality.