Figure 4 – Sensitivity analysis for the number of cyclists and the maximum number of iterations: a) Objective function variation; b) Number of objective function evaluations
The second sensitivity analysis focused on the variation of the cyclists’ mass. To this purpose, the minimum and maximum values considered were changed in a range from 25 to 125 kg. The results showed no significant difference. Only when all the cyclists had the same weight, regardless the value, the optimization became slower and with worse results. This lack of randomness in the cyclists’ weight hampers the exploration of points near the boundaries, since there are no lightweight cyclists, who are less affected by the gravitational force, to venture closer to the boundaries because of the penalties.
Finally, the score used to calculate the \(k_{g}\) and \(k_{d}\)coefficients of the ranked cyclists according to the power spent in each iteration was evaluated. The best cyclist, or the one who spends less energy, receives the higher score and the worst receives the minimum. Figure 5 shows that only with a combination of high values, the results of the objective function got worse. In terms of the number of evaluations of the objective function, no significant difference was observed. In all the analyses made, the GTA presented consistent results despite changes in its parameters. This robustness is very interesting for the user, since there is no need of heavy fine-tuning to adjust the default parameters so as to perform a better optimization.