Grey Wolf Optimizer for parameter estimation in surface waves

摘要

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. (C) 2015 Elsevier Ltd. All rights reserved.