## 4 Experimental Results and Discussion
### CLP Optimization Results  
Next, the integration of RF and PSO was developed to achieve the optimum value of the selected factors and maximum CLP value, according to the objective function in Equation (11). For this case study, the average value of each factor (\(F_{\alpha logi}\)) and CLP after normalization are shown in Table 4, and the average CLP value for the data set is 0.259. For the purpose of illustrating a CLP improvement trend, a sensitivity analysis was carried out to show the influence of different values of input parameters (namely \(\omega\) and \(CLP_{tgt}\)) on output variables (\(CLP_{Pred}\) and \(Z\)) for understanding the impact of input parameters on model output. Table 5 shows the results of the sensitivity analysis, which indicates the value of \(Z\) and predicted CLP as outputs based on different values of \(\omega\) and \(CLP_{tgt}\) as inputs of the RF-PSO model. The value of \(\omega\) was changed to between 0.27 and 1; \(\omega\) = 1 is the largest possible value for \(\omega\) and indicates that Goal 2 has no impact on the model. The \(CLP_{tgt}\) is in the range of 0.45 to 1, and \(CLP_{tgt}=1\) is the largest possible value for CLP resulting from the normalization process. Figure 3 is based on the results in Table 5, which shows the value of \(CLP_{Pred}\) for different values of \(\omega\) and \(CLP_{tgt}\). For a specific \(CLP_{tgt}\), by increasing \(\omega\), \(CLP_{Pred}\) increases, which shows the model sensitivity to \(\omega\), which is the relative importance of Goal 1. For \(CLP_{tgt}=0.45\) and \(CLP_{tgt}=0.6\), the changes in \(CLP_{Pred}\) are much less, given \(\omega\) greater than 0.4. So, it can be concluded that when \(CLP_{tgt}\) is less than or equal to 0.6, the most appropriate value of \(\omega\) is less than or equal to 0.4. This means the minimum deviation of \(F_{Predi}\) (predicted value of CLP factors) from \(F_{Avgi}\) (average value of CLP factors in the dataset) as a Goal 2 in Equation (10) has more weight, compared to the minimum deviation of \(CLP_{Pred}\) from \(CLP_{tgt}\) as a Goal 1 in Equation (9). \begin{table}
\begin{tabular}{c c} \hline \hline
**Selected Factor and CLP** & **Average Value in Normalized Data Set (\(F_{Avgi}\))** \\ \hline (1) Crew size & 0.302 \\ (2) Crew composition & 0.289 \\ (3) Treatment of craftsperson by foreman & 0.569 \\ (4) Craftsperson trust in foreman & 0.518 \\ (5) Level of interruption and disruption & 0.162 \\ (6) Complexity of task & 0.500 \\ (7) Working condition (dust and fumes) & 0.218 \\ (8) Location of work scope (elevation) & 0.132 \\ (9) Congestion of work area & 0.438 \\ (10) Fairness in performance review of crew by foreman & 0.694 \\ (11) Ground conditions & 0.368 \\ (12) Quality audits & 0.832 \\ (13) Risk monitoring and control & 0.264 \\ (14) Crisis management & 0.634 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average values of selected factors and CLP of the data set. For selecting the most appropriate weight and targeted CLP, a company's preference is important. Most companies prefer minimum deviation from \"average value of factors\", which is feasible to reach, helps them decrease the number of corrective measures that are required, and thus reduces the cost of implementing corrective measures. Based on this, Goal 2 needs to have more weight compared to Goal 1, which leads to selecting a value of \(\omega\) less than or equal to 0.5 as a weight of Goal 1. For this case study, the targeted CLP (\(CLP_{tgt}\)) of 0.75 and \(\omega\) of 0.27 were selected. Equation (14) indicates the objective function of the HFS-RF-PSO algorithm, according to the selected factors. In the presented algorithm, the settings were the number of particles = 50, maximum number of iterations = 30, and maximum velocity = 2, and the value of learning factors \(c_{1}\) and \(c_{2}\) were both set to 2.05. The initial values of the parameters were established on the basis of the relevant literature [66]. A large number of trials were performed to obtain the optimum values for this case.