## 4 Results  
To make a detailed explanation in this stage, we analyzed the extracted data from the final 105 studies after applying inclusion and exclusion criteria to answer the research questions. To start, we first summarized all the 27 unique metrics used in the studies, as shown in Table 2. Figure 2 shows the number of studies by publication year. RQ1: What are the studies that applied none or one or more performance metrics? In order to see how the existing studies used performance metrics, we grouped our collected studies based on the number of metrics used. After full reading, we found that the maximum number of metrics used in the nominated studies was six. Thus, our grouping strategy adopted these abbreviations: M0 means zero metrics, M1 for one metric, M2 for two metrics, M3 for three metrics, M4 for four metrics, M5 for five metrics, and M6 for six metrics. This means articles that had not employed or not reported the defined performance metrics would be listed in the M0 group and those with one metric in M1, etc. Figure 3 shows that most of the studies, based on this grouping, used zero metrics, which accounted for 37 articles, and the second rank deployed two metrics. Meanwhile, the graph shows a decline in the studies that employed more than two metrics. It is worth noting that only four studies employed six metrics (M6), and four others used five metrics (M5). However, to make it more meaningful, we needed to address what the dominant metrics were, thus creating another detail from this point by showing how they (the 27 metrics) were used over 105 studies. This would answer another RQ. RQ2: What metrics are most or least used in the studies? From this point of discovery, tallying of the used metrics is discussed. It is not surprising that some of the employed metrics were selected, due to their broad usage in the literature [10; 46]. We only tallied the sections of the studies that used performance metrics; hence, those using zero metrics were excluded, such as the M0 set. With the help of Excel spreadsheet visualization, the obtained result is presented in figures. Figure 4, which showsthe distribution of the metrics for the M1 group, which had a total of 19 articles, yet they employed one metric in each of the studies, and therefore, there were only two unique metrics involved, which were HV and IGD. However, IGD was only used once, while HV was used 18 times in this set of the M1 group. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Metric** & **Symbol** \\ \hline
1 & Hypervolume & HV \\
2 & Hypervolume ratio & HVR \\
3 & Hypervolume with R-metric & R-HV \\
4 & Pareto front size & PFS \\
5 & Number of non-dominated solutions & NDS \\
6 & Generalized spread & GS \\
7 & Error ratio & ER \\
8 & Inverted generational distance & IGD \\
9 & Generational distance & GD \\
10 & R-metric & R2 \\
11 & Maximum spread & MS \\
12 & Contribution metric & - \\
13 & Maximum Pareto front error & MPFE \\
14 & Hypercube-based diversity metric & - \\
15 & Spread: Delta measure & \(\Delta\) \\
16 & Convergence metric & CM \\
17 & Coverage difference & D \\
18 & Two set coverage & C \\
19 & Euclidean distance & ED \\
20 & Epsilon family & e \\
21 & Spacing & S \\
22 & Inverted generational distance & IGD+ \\
23 & Overall nondominated vector generation & ONVG \\
24 & Percentage & P \\
25 & Lp-norm-based diversity & Lp-norm \\
26 & Number of solutions in the region of interest & Proi \\
27 & Convergence measure & \(\rho\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: List of performance metrics. Figure 2: Number of studies by publication year. Figure 5 shows that a total of 24 articles employed sets of two metrics. Although the number of studies involved in this group (M2) was more than the previous one (M1), it comprised a good number of metrics (good diversity with a total of 11 unique metrics), and yet HV and IGD were the leading ones, which means they were the most used metrics.