# An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
## 5 Experimental Results
### Simulation Results  
After the optimisation stage, we have tested the best configurations calculated by the algorithms in 90 unseen scenarios to address the robustness of each optimisation result. The obtained values are shown in Table 4 where the measured distance to the rogue drone (D\({}_{\text{rd}}\star\)) is detailed, as well as the precision of the final shape achieved, indicating the percentage of UAVs presenting final positions with a five-percent deviation from the desired five-metre radius, and also a less restrictive condition, i.e., ten percent. It can be seen that a swarm of three UAVs achieved competitive results by using the three best configurations when tested on the 90 unseen scenarios, evidencing that it is the easiest of the case studies analysed. However, the swarm of five UAVs did perform differently, as only the configuration obtained by EA.dcx achieved the best results with 100% of UAVs placed into the five-percent tolerance at the end of the formation, followed by RS and EA.ucx (56.7% and 47.8%, respectively). Surprisingly, the configuration achieved by RS placed more UAVs in a stable shape than the EA.ucx's, given insights of a possible overfitting as EA.ucx did improve RS during the optimisation process. We believe that this situation may be improved by choosing different optimisation scenarios (diversity) or a bigger number of them, although this would imply the use of far more computational resources. Finally, in the most complex case study (10 UAVs), EA.ucx presented the most accurate results (93.3% of scenarios) followed by EA.dcx (51.1%), both arranging the UAVs in formations with a precision higher than 90%. The best configuration achieved by RS for 10 UAVs was unable to achieve precise sphere shape in any scenario. Due to the high complexity of this case study, no configuration obtained formations within plus/minus five-percent tolerance. From the metrics obtained after testing the best configurations on 90 unseen scenarios per case study, we can see that three UAVs do not require a very fine tuning of their parameters as formations were always achieved, even when using the configuration found by RS. When the problem becomes more difficult, only EA.dcx finds a configuration for five UAVs that obtains 100% of successful formations (plus/minus 5% accuracy). This completely discards RS as an option, and leaves EA.ucx to be used for less precise formations. Finally, in the most complex case study, i.e., 10 UAVs, only formations with a 10% accuracy were possible, with EA.ucx being the best choice for that (93.3% of the testing scenarios). This does not mean that it underperformed in the remaining scenarios. On the contrary, the reported minimum and maximum distances to the centre (D\({}_{\text{rd}}\star\)), show that despite the precision loss, the UAVs are still surrounding the rogue drone. This study thus confirms the necessity of testing the results from the optimisation of a given set of scenarios on different unseen conditions which, in the end, could give some insight about the robustness of the system. As a complement to the results presented in Table 4, Figure 11 shows the final positions of all the UAVs in the 90 scenarios per case study and algorithm. Despite the fact that