Open Access
ARTICLE
VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity
Junqiang Jiang1,2, Zhifang Sun1, Xiong Jiang1, Shengjie Jin1, Yinli Jiang3, Bo Fan1,*
1
School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang, 414006, China
2
Department of Computer and Information Science, Linköping University, Linköping, 58183, Sweden
3
Department of Safety and Quality, Hunan Construction Investment Group, Changsha, 410004, China
* Corresponding Author: Bo Fan. Email:
Computers, Materials & Continua 2023, 77(2), 1617-1644. https://doi.org/10.32604/cmc.2023.041973
Received 13 May 2023; Accepted 07 September 2023; Issue published 29 November 2023
Abstract
The grey wolf optimizer (GWO) is a swarm-based intelligence optimization algorithm by simulating the steps
of searching, encircling, and attacking prey in the process of wolf hunting. Along with its advantages of simple
principle and few parameters setting, GWO bears drawbacks such as low solution accuracy and slow convergence
speed. A few recent advanced GWOs are proposed to try to overcome these disadvantages. However, they are
either difficult to apply to large-scale problems due to high time complexity or easily lead to early convergence. To
solve the abovementioned issues, a high-accuracy variable grey wolf optimizer (VGWO) with low time complexity
is proposed in this study. VGWO first uses the symmetrical wolf strategy to generate an initial population of
individuals to lay the foundation for the global seek of the algorithm, and then inspired by the simulated annealing
algorithm and the differential evolution algorithm, a mutation operation for generating a new mutant individual is
performed on three wolves which are randomly selected in the current wolf individuals while after each iteration.
A vectorized Manhattan distance calculation method is specifically designed to evaluate the probability of selecting
the mutant individual based on its status in the current wolf population for the purpose of dynamically balancing
global search and fast convergence capability of VGWO. A series of experiments are conducted on 19 benchmark
functions from CEC2014 and CEC2020 and three real-world engineering cases. For 19 benchmark functions,
VGWO’s optimization results place first in 80% of comparisons to the state-of-art GWOs and the CEC2020
competition winner. A further evaluation based on the Friedman test, VGWO also outperforms all other algorithms
statistically in terms of robustness with a better average ranking value.
Keywords
Cite This Article
APA Style
Jiang, J., Sun, Z., Jiang, X., Jin, S., Jiang, Y. et al. (2023). VGWO: variant grey wolf optimizer with high accuracy and low time complexity. Computers, Materials & Continua, 77(2), 1617-1644. https://doi.org/10.32604/cmc.2023.041973
Vancouver Style
Jiang J, Sun Z, Jiang X, Jin S, Jiang Y, Fan B. VGWO: variant grey wolf optimizer with high accuracy and low time complexity. Computers Materials Continua . 2023;77(2):1617-1644 https://doi.org/10.32604/cmc.2023.041973
IEEE Style
J. Jiang, Z. Sun, X. Jiang, S. Jin, Y. Jiang, and B. Fan "VGWO: Variant Grey Wolf Optimizer with High Accuracy and Low Time Complexity," Computers Materials Continua , vol. 77, no. 2, pp. 1617-1644. 2023. https://doi.org/10.32604/cmc.2023.041973