Etminan Aslan, Moghaddam Mahta
Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA.
IEEE J Multiscale Multiphys Comput Tech. 2018;3:167-175. doi: 10.1109/JMMCT.2018.2875107. Epub 2018 Oct 10.
In this paper, we introduce a global optimization method that is a novel combination of the simulated annealing method and the multi-directional search algorithm. We demonstrate the use of the algorithm for a microwave-imaging system to obtain the electrical properties of objects. The proposed global optimizer significantly improves the performance and speed of the simulated annealing method by utilizing a nonlinear simplex search, starting from an initial guess, and taking effective steps in obtaining the global solution of the minimization problem. Due to the efficient performance of the proposed global optimization method, we are able to obtain the shape, location, and material properties of the target without considering any a priori information about them. The accuracy and applicability of the proposed imaging method is demonstrated with some numerical results in which two-dimensional images of multiple objects are successfully reconstructed.
在本文中，我们介绍了一种全局优化方法，它是模拟退火方法与多方向搜索算法的新颖结合。我们展示了该算法在微波成像系统中的应用，以获取物体的电学特性。通过利用非线性单纯形搜索，从初始猜测开始，并在获得最小化问题的全局解时采取有效步骤，所提出的全局优化器显著提高了模拟退火方法的性能和速度。由于所提出的全局优化方法具有高效的性能，我们能够在不考虑任何关于目标的先验信息的情况下获得目标的形状、位置和材料特性。通过一些数值结果证明了所提出成像方法的准确性和适用性，其中成功重建了多个物体的二维图像。
