An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

M. W. Guo; J. S. Wang; L. F. Zhu; S. S. Guo; W. Xie

doi:10.1109/ACCESS.2020.2984321

IEEE Access (Jan 2020)

An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve Function Optimization Problems

M. W. Guo,
J. S. Wang,
L. F. Zhu,
S. S. Guo,
W. Xie

Affiliations

M. W. Guo: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
J. S. Wang: ORCiD; School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
L. F. Zhu: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
S. S. Guo: ORCiD; School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China
W. Xie: School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

DOI: https://doi.org/10.1109/ACCESS.2020.2984321
Journal volume & issue: Vol. 8
pp. 69861 – 69893

Abstract

Read online

Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking. It is easy to fall into local optimum when used to optimize high-dimensional data, and there is imbalance between exploration and exploitation. An improved grey wolf optimizer based on tracking mode and seeking mode is proposed to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The algorithm is verified by simulation experiments in three parts. Firstly, the proposed grey wolf optimizer based on tracking mode (TGWO), the improved grey wolf optimizer based on seeking mode (SGWO), the improved grey wolf optimizer based on tracking and seeking mode (TSGWO), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO) are adopted to optimize 21 typical benchmark functions respectively, and the obtained statistical simulation results are compared; Secondly, the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer (BGWO), Hybrid PSOGWO Optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS); Finally, it is applied to the lightest design engineering problem of pressure vessels. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved grey wolf optimizer based on tracking mode and seeking mode can better solve function optimization and classical engineering problems with constraints. It was found the improved grey wolf optimizer based on tracking mode has the high precision and the characteristics of balanced exploration and exploitation.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

About the journal

Abstract

Keywords