Grey Wolf Optimizer for parameter estimation in surface waves

• 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.

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.