Faculty of Chemistry, Northeast Normal University, Changchun, People's Republic of China.
PLoS One. 2012;7(12):e51199. doi: 10.1371/journal.pone.0051199. Epub 2012 Dec 11.
Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces, and has been successfully used to solve several kinds of problems. In this paper, differential evolution is used for quantitative interpretation of self-potential data in geophysics. Six parameters are estimated including the electrical dipole moment, the depth of the source, the distance from the origin, the polarization angle and the regional coefficients. This study considers three kinds of data from Turkey: noise-free data, contaminated synthetic data, and Field example. The differential evolution and the corresponding model parameters are constructed as regards the number of the generations. Then, we show the vibration of the parameters at the vicinity of the low misfit area. Moreover, we show how the frequency distribution of each parameter is related to the number of the DE iteration. Experimental results show the DE can be used for solving the quantitative interpretation of self-potential data efficiently compared with previous methods.
差分进化（DE）是一种基于种群的进化算法，广泛用于解决连续空间中的多维全局优化问题，并已成功用于解决多种问题。在本文中，差分进化被用于地球物理学中的自电位数据的定量解释。估计了六个参数，包括电偶极矩、源的深度、原点的距离、极化角和区域系数。本研究考虑了来自土耳其的三种数据：无噪声数据、污染的合成数据和野外实例。差分进化及其对应模型参数的构建取决于生成的数量。然后，我们展示了参数在低拟合区域附近的振动情况。此外，我们还展示了每个参数的频率分布与 DE 迭代次数的关系。实验结果表明，与以前的方法相比，DE 可用于有效地解决自电位数据的定量解释问题。
