No abstract

Due to the approved applicability of differential evolution (DE) in geophysical problems, the algorithm has been widely concerned. The DE algorithms are mostly applied to solve the geophysical parametric estimation based on specific models, but they are rarely used in solving the physical property inverse problem of geophysical data. In this paper, an improved adaptive differential evolution is proposed to solve the lp norm magnetic inversion of 2D data, in which the perturbation direction in the mutation strategy is smoothed by using the moving average technique. Besides, a new way of updating the regularization coefficient is introduced to balance the effect of the model constraint adaptively. The inversion results of synthetic models demonstrate that the presented method can obtain a smoother solution and delineate the distributions of abnormal bodies better. In the field example of Zaohuoxi iron ore deposits in China, the reconstructed magnetic source distribution is in good agreement with the one inferred from drilling information. The result shows that the proposed method offers a valuable tool for magnetic anomaly inversion.

As a powerful optimization algorithm for solving nonlinear, complex and tough global optimization problems, differential evolution (DE) has been widely applied in various science and engineering fields. In this paper, considering that the evolution direction of each individual is not fully exploited to guide the search process in most DE algorithms, a new DE variant ( named ADEwSE), which incorporates the successful experience of evolved individuals into classic "current-to-pbest/1" mutation strategy to reduce the randomness of search direction, is proposed. Moreover, crossover matrix sorting scheme based on real crossover rate, opposition learning of crossover rate and adaptive adjustment of top p% values are combined with the new mutation strategy to improve the global search ability. In addition, to improve the searching ability of ADEwSE further, an ADEwSE variant by introducing the linear reduction of population size is proposed. In order to verify and analyze the performance of ADEwSE, numerical experiments on a set of 29 test problems from CEC2017 benchmark for 30, 50 and 100 dimensions are executed. And the experimental results are compared with that of 21 state-of-art DE-based algorithms. Comparative analysis indicates that the ADEwSE and its improved version are competitive with these stateof-art DE variants in terms of solution quality obtained.