Application of cascaded methods for inverse problem

Jhuo-Ro Li, C. Chiu, C. Chuang
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Abstract

In this paper, we propose a method, which combines a particle swarm optimization (PSO) algorithm with a Newton-Kantorovitch algorithm for image reconstruction of perfectly conducting objects. First, the inverse problem is recast as a global nonlinear optimization problem, which is solved by a PSO. Then, the solution obtained by the PSO is taken as an initial guess for the Newton-Kantorovitch algorithm to obtain the more accuracy solution in a few iterations. Numerical simulations are conducted to demonstrate that our cascaded method is accurate and practical. Numerical results show that the performance of this cascaded method is better than the individual PSO and the individual Newton-Kantorovitch algorithm. Satisfactory reconstruction has been obtained by using this cascaded method.
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级联方法在反问题中的应用
本文提出了一种结合粒子群优化算法和牛顿-坎托罗维奇算法的理想导电物体图像重建方法。首先,将逆问题转化为全局非线性优化问题,利用粒子群算法求解。然后,将粒子群算法得到的解作为Newton-Kantorovitch算法的初始猜测,在几次迭代中得到精度更高的解。数值模拟结果表明,该方法具有较好的精度和实用性。数值结果表明,该方法的性能优于单个粒子群算法和单个Newton-Kantorovitch算法。该方法得到了满意的重构结果。
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