A Hybridization Grey Wolf Optimizer to Identify Parameters of Helical Hydraulic Rotary Actuator

Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE al...

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Bibliographic Details
Published inActuators Vol. 12; no. 6; p. 220
Main Authors Zheng, Yukun, Sun, Ruyue, Liu, Yixiang, Wang, Yanhong, Song, Rui, Li, Yibin
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.06.2023
Subjects
Actuators
Algorithms
Comparative analysis
Control theory
Evolutionary computation
Friction
Genetic algorithms
grey wolf optimizer
helical hydraulic rotary actuators
Heuristic
Heuristic methods
Hybridization
Hydraulics
Least squares
Load
Mathematical models
Mathematical optimization
Mutation
opposition-based optimization
Optimization algorithms
Parameter identification
Particle swarm optimization
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Summary:Based on the grey wolf optimizer (GWO) and differential evolution (DE), a hybridization algorithm (H-GWO) is proposed to avoid the local optimum, improve the diversity of the population, and compromise the exploration and exploitation appropriately. The mutation and crossover principles of the DE algorithm are introduced into the GWO algorithm, and the opposition-based optimization learning technology is combined to update the GWO population to increase the population diversity. The algorithm is then benchmarked against nine typical test functions and compared with other state-of-the-art meta-heuristic algorithms such as particle swarm optimization (PSO), GWO, and DE. The results show that the proposed H-GWO algorithm can provide very competitive results. On this basis, the forgetting factor recursive least squares (FFRLS) method and the proposed H-GWO algorithm are combined to establish a parameter identification algorithm to identify parameters of the helical hydraulic rotary actuator (HHRA) with nonlinearity and uncertainty questions. In addition, the proposed method is verified by practical identification experiments. After comparison with the least squares (LS), recursive least squares (RLS), FFRLS, PSO, and GWO results, it can be concluded that the proposed method (H-GWO) has higher identification accuracy.
ISSN:2076-0825
2076-0825
DOI:10.3390/act12060220