strong conclusion. However, as the average performance is concerned (see bold horizontal line in the box plots), SPEA, MIGA and MOEA outperform others slightly and are more consistent in terms of the measure of UD**. Figure 21 shows the distribution of** *Neval* **(number of function** evaluation) performed by each algorithm in a specified time. More function evaluations in a fixed CPU time indirectly indicates that less CPU time is required by the algorithm. Intuitively, this means less computational efforts are required by the algorithm to find the trade-offs. As shown in Figure 21, MIGA requires the least algorithm effort while the performances of FFGA, NPGA and MOEA are moderate in terms of *Neval***. It can also be observed that SPEA and NGGA are suitable** for problems with time-consuming function evaluations: the effects in algorithm effort become less significant in these problems. In summary, the results show that MOEA requires moderate computational effort and exhibits a relatively good performance in terms of SSC and UD **on the test** problem, as compared to other MO evolutionary optimization methods in this study. 

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