Enhancing artificial bee colony algorithm using refraction principle
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METHODOLOGIES AND APPLICATION
Enhancing artificial bee colony algorithm using refraction principle Peng Shao1 · Le Yang1 · Liang Tan1 · Guangquan Li1 · Hu Peng2
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
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 oppositionbased 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. Keywords Evolutionary algorithms · Artificial bee colony algorithm · Opposition-based learning · Refraction principle
1 Introduction In real world, especially in nowadays with information technology developing rapidly, many fields have faced or are facing a variety of complex optimization problems. However, with the increasing complexity of problems, traditional optimization methods have been difficult to deal with them. Fortunately, evolutionary algorithms (EAs) have very good optimization performance over these complex problems and play a more and more important role in the field of optimization. In order to solve various complex optimization problems, more and more EAs have been proposed such as Communicated by V. Loia.
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Peng Shao [email protected]
1
School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, People’s Republic of China
2
School of Information Science and Technology, Jiujiang University, Jiujiang 332005, People’s Republic of China
particle swarm optimization (PSO) (Kennedy and Eberhart 1995), differential evolution (DE) (Storn and Price 1997), genetic algorithm (GA) (Go
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