The paper analyses the possibility of increasing efficiency of the Iterative Weighted Similarity Transformation (IWST) method, which is a prototype of classic robust methods, using global optimisation approach instead of classical one, available in the literature. For the purpose of solving the optimisation problem of the IWST method, in addition to the Iterative Reweighted Least Squares (IRLS) method, the Genetic algorithm (GA) and Generalised Particle Swarm Optimisation (GPSO) algorithm were applied, in order to overcome some flaws of IRLS method. Experimental research was performed based on the Monte Carlo simulation using the mean success rate (MSR) on the example of the geodetic control network for monitoring the Šelevrenac dam in the Republic of Serbia. By using the GA and GPSO algorithms, the overall efficiency of the IWST method has been increased by about 18% compared to the IRLS method. Also, it has been determined that the efficiency of the IRLS method significantly reduces with the increase in the number of displaced potential reference points (PRPs), while the GA and GPSO algorithms’ efficiency does not change significantly. The values of overall absolute true errors due to the increased number of displaced PRPs in the GA and GPSO algorithms did not change notably while with the IRLS method their values increased significantly.

本文分析了使用经典全局鲁棒方法代替经典鲁棒方法的原型的迭代加权相似性变换（IWST）方法的效率提高的可能性，该方法是文献中提供的。为了解决IWST方法的优化问题，除迭代加权最小二乘（IRLS）方法外，还应用了遗传算法（GA）和广义粒子群优化（GPSO）算法，以克服一些缺陷。 IRLS方法。在蒙特卡洛模拟的基础上，使用平均成功率（MSR），以大地测量控制网络为例，对塞尔维亚共和国的Šelevrenac大坝进行了监测，进行了实验研究。通过使用GA和GPSO算法，与IRLS方法相比，IWST方法的整体效率提高了约18％。此外，已经确定，IRLS方法的效率会随着位移的潜在参考点（PRP）数量的增加而显着降低，而GA和GPSO算法的效率不会发生明显变化。由于GA和GPSO算法中置换的PRP数量增加，总的绝对真实误差的值没有显着变化，而使用IRLS方法时，它们的值显着增加。