Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Lijun Sun; Binbin Feng; Tianfei Chen; Dongliang Zhao; Yan Xin

doi:10.1155/2022/2721490

Equalized Grey Wolf Optimizer with Refraction Opposite Learning

Comput Intell Neurosci. 2022 May 11:2022:2721490. doi: 10.1155/2022/2721490. eCollection 2022.

Authors

Lijun Sun^{1

2

3}, Binbin Feng^{1

2

3}, Tianfei Chen^{1

2

3}, Dongliang Zhao^{1

2

3}, Yan Xin^{1

2}

Affiliations

¹ Key Laboratory of Grain Information Processing and Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China.
² Zhengzhou Key Laboratory of Machine Perception and Intelligent System, Henan University of Technology, Zhengzhou 450001, China.
³ College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.

Abstract

Grey wolf optimizer (GWO) is a global search algorithm based on grey wolf hunting activity. However, the traditional GWO is prone to fall into local optimum, affecting the performance of the algorithm. Therefore, to solve this problem, an equalized grey wolf optimizer with refraction opposite learning (REGWO) is proposed in this study. In REGWO, the issue about the low swarm population variety of GWO in the late iteration is well overcome by the opposing learning of refraction. In addition, the equilibrium pool strategy reduces the likelihood of wolves going to the local extremum. To investigate the effectiveness of REGWO, it is evaluated on 21 widely used benchmark functions and IEEE CEC 2019 test functions. Experimental results show/ that REGWO performs better than the other competitors on most benchmarks.

MeSH terms

Algorithms*
Computer Simulation
Learning*
Probability