# An Improved Spider Wasp Optimizer for Green Vehicle Route Planning in Flower Collection
## 5 Numerical Experiments
### Experimental Analysis to Verify the Effectiveness of the Proposed Model  
To demonstrate that the proposed model can effectively reduce the total transportation costs and carbon emissions, optimization is performed using distance, \(C_{total}-c_{4}\) (excluding carbon emissions costs), and \(C_{total}\) as the objective functions, respectively. The experimental results are shown in Table 8, and the parameters in the table are explained as follows: VN is the total number of vehicles used; TC is the total transportation cost; TD is the total transportation distance; CF is the total fuel cost; CE is the total carbon emission cost; CD is the total flower loss cost; and PC is the total penalty cost for violating time windows. (1) When the optimization objective is distance, although the total travel distance of the vehicles is minimized, the number of vehicles used is maximized, leading to an increase of CNY 781.5 in total cost compared to the results with \(C_{total}\) as the optimization objective. This experiment indicates that the relationship between vehicle travel distance and total cost is not linear. Optimizing travel distance alone may lead to an increase in other costs. Therefore, it is unreasonable to consider vehicle travel distance in isolation during the route optimization process. (2) When the optimization objective is \(C_{total}-c_{4}\) (excluding carbon emissions costs), the transportation distance, carbon emissions, fuel costs, and flower loss costs all increase. In addition, under carbon tax policies, carbon emission costs have become a significant operational expense for businesses. Ignoring this factor may lead to an increase in the overall total cost. Therefore, carbon emission costs should be included in the optimization objective when planning vehicle routes. (3) When the optimization objective is \(C_{total}\), the total cost (TC) is minimized, and the number of vehicles used is lower compared to the distance-based optimization objective. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node** & **Longitude** & **Latitude** & **Demand (t)** & \(\boldsymbol{es_{i}\) (h)} & \(\boldsymbol{Is_{i}\) (h)} \\ \hline
0 & 102.8224928 & 24.8815021 & 0 & 0 & 12 \\
1 & 102.8195569 & 24.8827508 & 0.1 & 1.5 & 2.5 \\
2 & 102.8202998 & 24.8821707 & 0.2 & 5.0 & 6.0 \\
3 & 102.8175754 & 24.8831819 & 0.1 & 3.0 & 4.0 \\
4 & 102.8198392 & 24.8841454 & 0.3 & 1.0 & 2.0 \\
5 & 102.8209623 & 24.8853125 & 0.2 & 4.0 & 5.5 \\
6 & 102.8208696 & 24.8861 & 0.3 & 0.5 & 1.5 \\
7 & 102.8180981 & 24.8857677 & 0.2 & 1.5 & 2.5 \\
8 & 102.8192316 & 24.8861 & 0.2 & 3.0 & 4.0 \\
9 & 102.817938 & 24.8861 & 0.1 & 4.5 & 6.0 \\
10 & 102.8235139 & 24.8797253 & 0.1 & 0.0 & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Warehouse center and customer information. The other parameters are also lower than those obtained with the \(C_{total}-c_{4}\) optimization objective. These experimental results confirm the effectiveness of the proposed optimization model in reducing both total transportation costs and carbon emissions. Experimental Analysis to Verify the Effectiveness of Solutions Generated by Random Generation and Nearest Neighbor Algorithms  
To test whether the initial populations generated by random generation and nearest neighbor algorithms can improve the algorithm's performance, this paper compares the Random Genetic Neighborhood Comprehensive Spider Wasp Algorithm (RGN_CSWA), which uses randomly generated initial solutions, with the GN_CSWA. The average total cost reported in Table 9 represents the mean value obtained from 10 independent runs of each algorithm on real map instances. Compared with the RGN_CSWA, the GN_CSWA achieves a 1.3% reduction in average total cost. To verify the significance of the experimental results, a significance test was conducted on all total cost data, with the significance level set to \(\alpha_{3}=0.05\).