Enhancing artificial bee colony algorithm using refraction principle

Abstract

The artificial bee colony algorithm (ABC), as one of the excellent intelligent optimization technologies, has presented very good optimization performance for many complex problems due to its simplicity and easiness of implementation. However, ABC has a very good performance at exploration relatively, but for some complex problems it still results in slower convergent speed and lower convergent accuracy in the later stage of algorithms. Meanwhile, ABC has relatively poor performance at exploitation. To overcome these drawbacks further, the enhancing ABC algorithm using refraction principle is proposed (EABC-RP) in this paper. In EABC-RP, on the one hand, in order to enhance its exploration further, the unified opposition-based learning (UOBL) based on refraction principle is employed to generate refraction solutions (new food sources) for employed bees, which helps to increase population diversity and guide search direction close to the global optimal solution. On the other hand, for exploitation, when ABC has fallen into the local optimal solution, the UOBL based on refraction principle is employed for mutation to increase the probability of jumping out of the local optimal solution for scout bees. A lot of experiments are conducted on 23 benchmark functions to verify the effectiveness of EABC-RP. The experimental results show that EABC-RP achieves higher solution accuracy and faster convergent speed in most cases and outperforms other ABC variants. In addition, EABC-RP is used to optimize finite impulse response (FIR) low-pass digital filter which obtains the better filtering performance, which validates the effectiveness of the EABC-RP algorithm further.