A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Grey Wolf Optimizer for parameter estimation in surface waves
Abstract This research proposed a novel and powerful surface wave dispersion curve inversion scheme called Grey Wolf Optimizer (GWO) inspired by the particular leadership hierarchy and hunting behavior of grey wolves in nature. The proposed strategy is benchmarked on noise-free, noisy, and field data. For verification, the results of the GWO algorithm are compared to genetic algorithm (GA), the hybrid algorithm (PSOGSA)-the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA), and gradient-based algorithm. Results from both synthetic and real data demonstrate that GWO applied to surface wave analysis can show a good balance between exploration and exploitation that results in high local optima avoidance and a very fast convergence simultaneously. The great advantages of GWO are that the algorithm is simple, flexible, robust and easy to implement. Also there are fewer control parameters to tune.
Highlights We proposed and tested a novel and powerful surface wave inversion scheme. The proposed strategy is called Grey Wolf Optimizer (GWO). The performance of GWO is tested on noise-free, noisy and field data. The results of GWO are compared to GA, PSOGSA, and MASW. Results show GWO can be effectively used for parameter estimation in surface waves.
Grey Wolf Optimizer for parameter estimation in surface waves
Abstract This research proposed a novel and powerful surface wave dispersion curve inversion scheme called Grey Wolf Optimizer (GWO) inspired by the particular leadership hierarchy and hunting behavior of grey wolves in nature. The proposed strategy is benchmarked on noise-free, noisy, and field data. For verification, the results of the GWO algorithm are compared to genetic algorithm (GA), the hybrid algorithm (PSOGSA)-the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA), and gradient-based algorithm. Results from both synthetic and real data demonstrate that GWO applied to surface wave analysis can show a good balance between exploration and exploitation that results in high local optima avoidance and a very fast convergence simultaneously. The great advantages of GWO are that the algorithm is simple, flexible, robust and easy to implement. Also there are fewer control parameters to tune.
Highlights We proposed and tested a novel and powerful surface wave inversion scheme. The proposed strategy is called Grey Wolf Optimizer (GWO). The performance of GWO is tested on noise-free, noisy and field data. The results of GWO are compared to GA, PSOGSA, and MASW. Results show GWO can be effectively used for parameter estimation in surface waves.
Grey Wolf Optimizer for parameter estimation in surface waves
Song, Xianhai (author) / Tang, Li (author) / Zhao, Sutao (author) / Zhang, Xueqiang (author) / Li, Lei (author) / Huang, Jianquan (author) / Cai, Wei (author)
Soil Dynamics and Earthquake Engineering ; 75 ; 147-157
2015-04-08
11 pages
Article (Journal)
Electronic Resource
English
Grey Wolf Optimizer for parameter estimation in surface waves
| Online Contents | 2015
Grey Wolf Optimizer for parameter estimation in surface waves
| British Library Online Contents | 2015
Parameter Extraction of Three Diode Solar Photovoltaic Model Using Improved Grey Wolf Optimizer
| DOAJ | 2021
A Comprehensive Review on Applications of Grey Wolf Optimizer in Energy Systems
| Springer Verlag | 2024
Advanced Damage Monitoring in Beam Structures Using Grey Wolf Optimizer and Additional Masses
| DOAJ | 2025