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.