Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21101 Novi Sad, Serbia.
Faculty of Civil Engineering, University of Montenegro, Bulevar Džordža Vašingtona bb, 81000 Podgorica, Montenegro.
Sensors (Basel). 2021 Dec 27;22(1):159. doi: 10.3390/s22010159.
In this paper, an original modification of the generalised robust estimation of deformation from observation differences (GREDOD) method is presented with the application of two evolutionary optimisation algorithms, the genetic algorithm (GA) and generalised particle swarm optimisation (GPSO), in the procedure of robust estimation of the displacement vector. The iterative reweighted least-squares (IRLS) method is traditionally used to perform robust estimation of the displacement vector, i.e., to determine the optimal datum solution of the displacement vector. In order to overcome the main flaw of the IRLS method, namely, the inability to determine the global optimal datum solution of the displacement vector if displaced points appear in the set of datum network points, the application of the GA and GPSO algorithms, which are powerful global optimisation techniques, is proposed for the robust estimation of the displacement vector. A thorough and comprehensive experimental analysis of the proposed modification of the GREDOD method was conducted based on Monte Carlo simulations with the application of the mean success rate (MSR). A comparative analysis of the traditional approach using IRLS, the proposed modification based on the GA and GPSO algorithms and one recent modification of the iterative weighted similarity transformation (IWST) method based on evolutionary optimisation techniques is also presented. The obtained results confirmed the quality and practical usefulness of the presented modification of the GREDOD method, since it increased the overall efficiency by about 18% and can provide more reliable results for projects dealing with the deformation analysis of engineering facilities and parts of the Earth's crust surface.
本文提出了一种通用变形观测差分稳健估计方法（GREDOD）的原始改进，应用两种进化优化算法，遗传算法（GA）和广义粒子群优化（GPSO），进行位移向量的稳健估计。传统上使用迭代重加权最小二乘法（IRLS）方法来进行位移向量的稳健估计，即确定位移向量的最优基准解。为了克服 IRLS 方法的主要缺陷，即如果有位移点出现在基准网络点集，则无法确定位移向量的全局最优基准解，因此提出了应用 GA 和 GPSO 算法进行位移向量的稳健估计，这两种算法都是强大的全局优化技术。本文还基于蒙特卡罗模拟应用平均成功率（MSR）对所提出的 GREDOD 方法的改进进行了全面深入的实验分析。对传统的 IRLS 方法、基于 GA 和 GPSO 算法的改进方法以及基于进化优化技术的最近的迭代加权相似变换（IWST）方法的改进方法进行了比较分析。得到的结果证实了所提出的 GREDOD 方法的改进具有良好的质量和实际应用价值，因为它的整体效率提高了约 18%，可以为处理工程设施和地壳表面部分变形分析的项目提供更可靠的结果。
