## 3 Results
### Comparison of the Performance of the Algorithm between Networks  
In this set of tests, we did not use brute force to find the absolute optimum as, considering the numbers of points and variants, it would have been too computationally demanding. Hence, in this and the following tests, the individual algorithms will only be mutually compared relative to the results of the combined A\({}_{\text{OPT}}\) (which, principally, has to always provide the best results) for the individual test and network variant. Thus, the results shown in Table 7 indicate which of the individual algorithms (A1-A4) performed the best and the worst.  
Table 7 confirms the conclusion implied by the testing of the square-like network using brute force--the performance of none of the individual algorithms could be considered satisfactory as none of them (perhaps with the exception of the two simplest networks--square-like and triangular, where the A4 algorithm performed very well) consistently provided the best results. More detailed results are given in Table A3 (Appendix C).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **Criterion** & **A1** & **A2** & **A3** & **A4** & **A\({}_{\text{OPT}}\)** \\ \hline \multirow{2}{*}{1.1—High accuracy required} & MNP & 104 & 110 & 102 & 108 & 101 \\  & ORD & 64 & 24 & 78 & 26 & 96 \\ \hline \multirow{2}{*}{1.2—Low accuracy required} & MNP & 119 & 107 & 106 & 104 & 100 \\  & ORD & 28 & 72 & 56 & 72 & 100 \\ \hline \multirow{2}{*}{1.3—Direction/angles more accurate than distances} & MNP & 107 & 110 & 102 & 106 & 101 \\  & ORD & 48 & 32 & 74 & 40 & 94 \\ \hline \multirow{2}{*}{1.4—Distances more accurate than directions/angles} & MNP & 105 & 109 & 105 & 107 & 102 \\  & ORD & 52 & 26 & 42 & 32 & 82 \\ \multirow{2}{*}{Average results} & MNP & 109 & 109 & 104 & 106 & 101 \\  & ORD & 48 & 39 & 63 & 43 & 93 \\ \hline \hline \end{tabular} MNP—Mean normalized (to the brute-force-acquired optimum) performance of the algorithm; ORD—percentage of successful detection of the optimal result.  
\end{table}
Table 6: Comparison of the results of optimization algorithms and brute-force-acquired absolute optimum for the square-like network [%].  
Comparison of the Algorithms when Testing Changes in Required Resulting Accuracies (LSEE) of Individual Networks  
The mean performances of each algorithm over all 20 steps of required accuracy are shown in Table 8. At first sight, it appears that the simplest algorithm (A4) generally performs the best, and if using only a single strategy, it would probably be the strategy of choice. However, the fact that the mean values are over 100% indicates that there are quite a few occasions where one of the other algorithms performs better.  
To be able to investigate this issue further, the results are also presented in the form of graphs (Figure 4). The horizontal axis shows the accuracy requirement (LSEE) for the particular network, while the vertical axis shows the MNP.  
As revealed in the previous tests, none of the proposed algorithms was capable of consistently producing the best results across networks and accuracies. Generally, we can observe that the algorithms with initial network configuration perform better at higher accuracy requirements; the opposite is typically true when relatively lower accuracies of 1.5 mm and more are required. The only exception can be observed in the triangular network for the A4 algorithm, which performed best across the entire accuracy range (although there were occasions where other algorithms performed better as obvious from the fact that the line does not copy the horizontal axis). More detailed results are given in Table A3 (Appendix C).