# Improved Genetic Algorithm for Solving Green Path Models of Concrete Trucks
## 4 Simulation Experiment
### Experimental Analysis  
After comprehensive consideration of \(\text{CV}_{\text{Mv}}\) and \(\text{FV}_{\text{Mv}}\), the range analysis of the test was carried out, as shown in Table 11, and the experiment results were shown in Figure 6. The specific calculation formula of CV is shown below (CV: comprehensive value). \[\text{CV}=0.3\text{CA}_{\text{Mv}}+0.7\text{FC}_{\text{Mv}}\] \begin{table}
\begin{tabular}{c c c c c c c} \hline
**NO.** & **A:**\(\alpha\) & **B:**\(\mathbf{p_{c}}\) & **C:**\(\mathbf{p_{m}}\) & **D:**\(\mathbf{\lambda}\) & **CV** \\ \hline
1 & 4 & 0.55 & 0.03 & 0.8 & 0.669 \\
2 & 4 & 0.6 & 0.04 & 0.85 & 0.821 \\
3 & 4 & 0.65 & 0.05 & 0.9 & 0.622 \\
4 & 4 & 0.7 & 0.06 & 0.95 & 0.890 \\
5 & 8 & 0.55 & 0.04 & 0.9 & 0.396 \\
6 & 8 & 0.6 & 0.03 & 0.95 & 0.861 \\
7 & 8 & 0.65 & 0.06 & 0.8 & 0.436 \\
8 & 8 & 0.7 & 0.05 & 0.85 & 0.184 \\
9 & 12 & 0.55 & 0.05 & 0.95 & 0.305 \\
10 & 12 & 0.6 & 0.06 & 0.9 & 0.205 \\
11 & 12 & 0.65 & 0.03 & 0.85 & 0.424 \\
12 & 12 & 0.7 & 0.04 & 0.8 & 0.681 \\
13 & 16 & 0.55 & 0.06 & 0.85 & 0.145 \\
14 & 16 & 0.6 & 0.05 & 0.8 & 0.625 \\
15 & 16 & 0.65 & 0.04 & 0.95 & 0.715 \\
16 & 16 & 0.7 & 0.03 & 0.9 & 0.578 \\ \(k_{lj}\) & 3.002 & 1.515 & 2.531 & 2.411 & \\ \(k_{2j}\) & 1.878 & 2.511 & 2.614 & 1.573 & \\ \(k_{3j}\) & 1.615 & 2.197 & 1.737 & 1.801 & \\ \(K_{4j}\) & 2.063 & 2.334 & 1.676 & 2.772 & \\ \(R_{j}\) & 1.387 & 0.996 & 0.937 & 1.199 & \\ \hline \end{tabular}
\end{table} 
Table 10: \(\text{L}_{16}(4^{4})\) orthogonal experimental array. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**NO. ** & \(\alpha\) & \(\mathbf{p_{c}}\) & \(\mathbf{p_{m}}\) & \(\mathbf{\lambda}\) & **CA** & \(\mathbf{CA_{Mv}}\) & **FC** & \(\mathbf{FC_{Mv}}\) \\ \hline
1 & 1 & 1 & 1 & 1 & 215.00 & 0.195 & 235.000 & 0.872 \\
2 & 1 & 2 & 2 & 2 & 248.00 & 1.000 & 230.000 & 0.744 \\
3 & 1 & 3 & 3 & 3 & 216.00 & 0.220 & 232.000 & 0.795 \\
4 & 1 & 4 & 4 & 4 & 233.00 & 0.634 & 240.000 & 1.000 \\
5 & 2 It can be seen from the Table 11 that K3A \(<\) K2A \(<\) K4A \(<\) K1A, K1B \(<\) K3B \(<\) K4B \(<\) K2B, K4C \(<\) K3C \(<\) K1C \(<\) K2C, K2D \(<\) K3D \(<\) K1D \(<\) K4D; therefore, the optimal levels of \(\alpha\), P\({}_{c^{\prime}}\) P\({}_{m}\), and \(\lambda\) are 12, 0.55, 0.06, and 0.85. The range values RC \(<\) RB \(<\) RD \(<\) RA; therefore, the value of the mutation probability has the greatest impact on the performance of the improved genetic algorithm. Through the results of range analysis, we can obtain the best horizontal combination: \(\alpha=12\), P\({}_{c}=0.55\), (p\({}_{c1}=0.4\), p\({}_{c2}=0.7\)), p\({}_{m}=0.06\), (p\({}_{m1}=0.025\), P\({}_{m2}=0.095\)), and \(\lambda=0.85\). From Figure 8, it can be seen that Experiment No. 13 produced the best results; in order to analyze the performance of the improved genetic algorithm under the optimal level combination, we solved the same enterprise order using the standard genetic algorithm, the improved genetic algorithm in Experiment No. 13, and the improved genetic algorithm under the optimal level combination. When solved by the standard genetic algorithm with P\({}_{c}=0.65\) and P\({}_{m}=0.05\), the final experimental results are shown in Figure 9.  
From Figure 9, it can be seen that the standard GA converges in approximately 234 generations, while the improved GA converges in 172 generations. Compared with the standard genetic algorithm, the improved genetic algorithm obtains lower total fuel consumption, and the improved genetic algorithm with the best combination of levels obtains the lowest total fuel consumption. The following conclusions can be drawn from the iterative curves of the three algorithms in the figure above:  
1. Compared with the standard GA, the IGA has a stronger convergence ability to generate high-quality individuals in the early stage of the algorithm. Figure 8: Experiment results. Figure 9: Algorithm iteration curve. 2. The IGA converged the solution in 172 iterations and had a 26.45% faster convergence than the standard GA.
3.