An adaptive unified artificial bee colony algorithm for global optimization

Abstract

The artificial bee colony (ABC) algorithm invented by Karaboga is a relative new nature inspired heuristic for optimization problem. It has been proved to be competitive with some conventional optimization algorithms. This paper proposes an adaptive unified artificial bee colony (auABC) algorithm which employs a single equation unifying multiple strategies into one expression. As we all know, ABC is good at exploration but poor at exploitation due to the insufficiency in its solution search equation and its one-dimensional search strategy leads to slower convergence speed. In order to improve the above defects, we created a combined self-adaptive equation that its parameters are determined by the current iteration times to solve the problem mentioned above. We also introduce a chaotic strategy when generating candidate food sources which balance the proportion of exploration and exploitation. Experiments conducted on benchmark functions demonstrate that our algorithm achieves good performance in both unimodal and multimodal functions as expected compared to several state-of-the-art algorithms.