Figure compares the numbers of solutions on the Pareto front for different algorithms and noise settings. On average, qNEHVI and MO-E-EQI-2 give more solutions on the Pareto front for 20 optimization steps. Fewer solutions were ob- served when the noise level was raised (for cases of Linear-4 and Loglinear-2). By using a mix of aggressive and non-aggressive criteria of MO-E-EQI, augmenting the current Pareto front is encouraged by the algorithm, therefore, an increased number of solutions for MO-E-EQI-2 were found compared to MO-E-EQI-1. In addition, running the MO-E-EQI longer with 40 steps lead to an improved number of Pareto front solutions in general. For relatively low noise MO-E-EQI-3 found more solutions on the Pareto front (average 16, 12, 10, 16 for noise Linear 1-3 and Loglinear-1, respectively), comparing to MO-E-EQI-2 (average 9, 8, 7, 8 for noise Linear 1-3 and Loglinear-1, respectively). When the noise level is high for Linear-4 and Loglinear-2, the number of solutions did not show obvious improvement when increasing the optimization steps.