## 6 Simulation Experimental for Multimodal Vehicle Logistics
### Analysis of Experimental Results  
W substituted the relevant parameters into the model constructed in Chapter 2. Then, we used the MVO [30], ALO [31], SCA [32], GWO [33], SCSO [17], MSCSO [20], and HSCSO algorithms to solve the model separately. The parameter settings of all of the algorithms are the same as those in Table 2, which refers to the CEC2015 benchmark suite. In the modeling of the SCSO algorithm, the problem dimension is explicitly defined as twice the number of cities, representing different city nodes and transportation modes. Each algorithm was repeated 500 times, and the iterative results in this example are illustrated in Figure 6, where the vertical axis represents the average value of the total cost. The optimal delivery routes and lowest costs obtained are shown in Figure 6 and Table 6. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Path** & **Nodes** & **Min Cost (USD)** & **Mean Cost (USD)** & **Std Cost (USD)** \\ \hline HSCSO & Shenyang-Dalian & Railway–Waterway & & 24,054.0 & 24,797.9 & 650.8 \\  & –Shanghai–Jiaxing & –Road & & 24,054.0 & 26,097.6 & 1610.2 \\ MSCSO & Shenyang-Dalian & Railway–Waterway & & & 24,441.9 & 26,179.6 & 1495.3 \\ SCSO & –Suzhou–Jiaxing & –Road & & & 24,533.5 & 25,895.0 & 1467.2 \\ MVO & Shenyang–Zaozhuang & Railway–Railway & & & 24,054.0 & 25,069.4 & 731.3 \\  & –Jiaxing & Railway–Railway & & & 24,441.9 & 26,562.4 & 1507.1 \\ ALO & –Shanghai–Jiaxing & –Road & & & 24,533.5 & 24,846.6 & 908.2 \\ GWO & –Suzhou–Jiaxing & –Road & & & & \\ SCA & Shenyang–Zaozhuang & Railway–Railway & & & & \\  & –Jiaxing & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detailed experimental results. Figure 6: The best results of HSCSO, MSCSO, CSCO, MVO, ALO, GWO, and SCA. As the test results show, all the algorithms (HSCSO, MSCSO, SCSO, MVO, ALO, GWO, and SCA) obtained the feasible transportation path. Among them, HSCSO, MSCSO, and ALO all found the optimal transportation path with a minimum total cost of 24,054.0 USD. However, the average solution value of HSCSO in each experiment was superior to that of the other algorithms, indicating that HSCSO has a stronger global search capability and consistently finds solutions close to the optimum in different experiments. Moreover, the significantly lower standard deviation of HSCSO suggests that it has good robustness. From the convergence condition, we can see that the initial solution cost of HSCSO is already minimized, benefiting from the optimization of the initial population through Logistic-Tent chaotic mapping. And among the seven algorithms, HSCSO takes more iterations to reach convergence than SCSO, MVO, and GWO. This is mainly because the lens opposition-based learning strategy is introduced in the proposed algorithm during the iteration process, which increases the richness of the population. In addition, the introduction of bellicosity factors and elite cross-pools also accelerates the convergence of the algorithm to a certain extent, alleviating the problem of being difficult to converge. For offline path planning problems, in order to obtain a better result, a longer algorithm running time is acceptable. In general, HSCSO balances the requirements of the convergence accuracy and convergence speed, and has effectiveness and superiority in reducing costs through multimodal transport route planning. The detailed comparison of the costs of each algorithm solution is shown in Figure 7. The optimal path diagrams solved by MVO, SCSO, and HSCSO are shown in Figure 8.  
From the comparison, it can be observed that the result of HSCSO makes full use of different modes of transportation and increases the transit cost, but greatly reduces the transportation cost. Equally important, the route planning results of HSCSO effectively suppressed fuel consumption and carbon emissions.