# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 6 Experimental Results and Analysis
### IPSO Algorithm Performance Test  
To verify the superiority of the IPSO algorithm, the PSO and IPSO algorithms were executed on eight benchmark test functions. The population size for the PSO and IPSO algorithms was set to 50, the number of iterations was set to 100, the dimensionality was set to 30, and the number of independent runs was set to 20. As shown in Table 4, the minima, maxima, averages, and standard deviations obtained by the IPSO algorithm were better than those obtained by the PSO algorithm and were closer to the global optimum. Figure 8 compares the convergence curves of the PSO and IPSO algorithms, and one can draw the following conclusions: the IPSO algorithm performs better in terms of convergence accuracy, and it enables the particles to gather more stably in the vicinity of the global optimum, therefore improving the ability of the PSO algorithm to overcome local extreme values.  
Figure 8: Comparison of convergence curves for eight test functions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Function** & **Arithmetic** & **Min** & **Max** & **Average** & **STD** \\ \hline \multirow{2}{*}{Sphere} & PSO & \(2.23\times 10^{-1}\) & \(1.12\) & \(5.53\times 10^{-1}\) & \(2.24\times 10^{-1}\) \\  & IPSO & \(3.62\times 10^{-30}\) & \(3.32\times 10^{-26}\) & \(1.46\times 10^{-26}\) & \(1.70\times 10^{-27}\) \\ \hline \multirow{2}{*}{Schweefel} & PSO & \(1.22\) & \(4.05\) & \(1.97\) & \(6.30\times 10^{-1}\) \\  & IPSO & \(1.28\times 10^{-15}\) & \(3.61\times 10^{-13}\) & \(3.64\times 10^{-14}\) & \(9.27\times 10^{-13}\) \\ \hline \multirow{2}{*}{Sum squares} & PSO & \(5.67\) & \(2.20\times 10^{1}\) & \(1.16\times 10^{1}\) & \(5.06\) \\  & IPSO & \(6.48\times 10^{-30}\) & \(5.78\times 10^{-26}\) & \(2.89\times 10^{-26}\) & \(3.97\times 10^{-27}\) \\ \hline \multirow{2}{*}{[PERSON]} & PSO & \(6.18\times 10^{1}\) & \(2.68\times 10^{2}\) & \(1.26\times 10^{2}\) & \(5.16\times 10^{1}\) \\  & IPSO & \(2.87\times 10^{1}\) & \(2.90\times 10^{1}\) & \(2.89\times 10^{1}\) & \(3.76\times 10^{-2}\) \\ \hline \multirow{2}{*}{[PERSON]} & PSO & \(4.73\times 10^{1}\) & \(1.01\times 10^{2}\) & \(7.36\times 10^{1}\) & \(1.45\times 10^{1}\) \\  & IPSO & \(1.82\times 10^{-1}\) & \(1.35\times 10^{1}\) & \(6.46\) & \(3.79\) \\ \hline \multirow{2}{*}{[PERSON]} & PSO & \(7.91\times 10^{-1}\) & \(3.18\) & \(2.28\) & \(4.75\times 10^{-1}\) \\  & IPSO & \(8.88\times 10^{-16}\) & \(8.88\times 10^{-16}\) & \(8.88\times 10^{-16}\) & \(0\) \\ \hline \multirow{2}{*}{[PERSON]} & PSO & \(2.43\times 10^{-2}\) & \(1.04\times 10^{-1}\) & \(4.86\times 10^{-2}\) & \(1.68\times 10^{-2}\) \\  & IPSO & \(2.84\times 10^{-4}\) & \(7.60\times 10^{-3}\) & \(8.78\times 10^{-4}\) & \(1.60\times 10^{-3}\) \\ \hline \multirow{2}{*}{Penalised} & PSO & \(1.71\times 10^{-2}\) & \(2.45\times 10^{-1}\) & \(9.51\times 10^{-2}\) & \(6.30\times 10^{-2}\) \\  & IPSO & \(2.79\times 10^{-5}\) & \(2.62\times 10^{-4}\) & \(1.53\times 10^{-4}\) & \(5.61\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of PSO and IPSO algorithm test results.