Adaptive Quantum Genetic Inversion Algorithm for One-Dimensional Magnetotelluric Inverse Problem
Adaptive Quantum Genetic Inversion Algorithm for One-Dimensional Magnetotelluric Inverse Problem
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发表时间：
2009
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通讯作者：
Zhang Xu-hui
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作者：
Zhang Xu-hui
本文应用传统的量子遗传算法（QGA）来解决分层模型的非线性大地电磁反演问题。然而，在我们的数值实验中，传统的QGA表现出了早熟收敛的问题。为了克服早熟收敛的缺点，我们对传统的QGA进行了改进，自动调整不同尺度的模型空间大小，最终提出了一种新的大地电磁数据反演方法，称为自适应量子遗传算法（AQGA）。AQGA方法的有效性为通过一些优化测试函数和合成大地电磁模型进行了验证。结果表明，AQGA减轻了早熟收敛，提高了反演模型的效率和精度。利用AQGA获得的大地电磁场数据模型与地质结构吻合较好，说明改进的AQGA方法对于非线性优化问题具有强大的作用。
This paper applied the conventional quantum genetic algorithm(QGA)to solve the nonlinear magnetotelluric inverse problem of layered model.However,the conventional QGA shows a premature convergence problem throughout our numerical experiments.In order to overcome the shortcoming of premature convergence,we improved the conventional QGA with automatically adjusting the size of model space with different scales,and eventually developed a novel method,referred as to adaptive quantum genetic algorithm(AQGA),for the inversion of magnetotelluric data.The validity of AQGA method is demonstrated by some optimization test functions and synthetic magnetotelluric models.The results show that AQGA mitigate the premature convergence and improve the efficiency and accuracy of inverted models.The obtained models using AQGA for magnetotelluric field data are well agreed with geological structure,which inferred that the improved AQGA method is powerful for the nonlinear optimization problem.