Improved artificial bee colony algorithm with randomized Halton sequence

The artificial bee colony (ABC) algorithm has gained popularity for solutions to many real-world complex problems because of its simplicity and efficiency. However, it is good at exploration but poor at exploitation and early gets trapped into local optimum in some case, which prevents the artificial bee colony algorithm from finding the final result accuracy and efficiently for complex problems. It's known to all that population initialization is an important factor affecting convergence performance of ABC. In this paper, a modified artificial bee colony algorithm with randomized Halton sequence was proposed. A series of experiments were performed on benchmark functions to testify the superiority of our proposed algorithm, and results were compared with other initialization algorithms including opposition-based learning, random initialization and chaotic initialization. The results indicate that our proposed algorithm provides higher solution accuracy and faster convergence speed.