LAUSR

The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.