## 5 Arithmetic Example
### Comparison of Different Algorithms
Figure 12: AGA algorithm cost target iteration curve. Figure 13: AGA algorithm time target iteration curve. Figure 15 shows the optimization path of the algorithm in [10]. Nanchang is the starting city, and Berlin is the destination city. It passes through Wuhan, Zhengzhou, Xi'an, Huaihua, Nanning, Beihai, Kunming, Guangzhou, Shanghai, Manchuria, Erenhot, Alataw Pass, Kashgar, Gwadar, Yangon, Bangkok, Singapore, Rotterdam, Hamburg, Warsaw, and Duisburg. According to Table 9, it is concluded that in the scheme of using the elite genetic algorithm for multimodal transport from Nanchang to Berlin, the conversion of transport mode to highway transport occurs from from Erenhot to Allah Pass, and highway transport is used from Warsaw to Duisburg. Other modes of transport are rail transport. Figures 16-18 shows the convergence curve of the iterative process of cost, time, and carbon emission target values. It can be seen that the cost target is optimal at about CNY 92,394 in 260 iterations. Similarly, the time target is optimal at about 780 h in 325 iterations, and the carbon emissions target is optimal at about 99,105 kg in 250 iterations. Figure 19 shows the path optimized by the FAGA algorithm, with Nanchang as the starting city and Berlin as the destination city, through Wuhan, Zhengzhou, Chongqing, Huaihua, Nanning, Beihai, Kunming, Guangzhou, Shanghai, Manzhouli, Erenhot, Alataw pass, Kashgar, Gwadar, Yangon, Bangkok, Singapore, Rotterdam, Hamburg, Warsaw, and Duisburg, a total of 21 cities. According to Table 10, it is concluded that in the scheme of using the FAGA algorithm for the multimodal transport from Nanchang to Berlin, the mode of transport is switched to highway transport from Guangzhou to Shanghai, from  
Figure 14: AGA algorithm carbon emissions target iteration curve. \begin{table}
\begin{tabular}{c c c} \hline \hline
**City1** & **Mode of Transportation** & **City2** \\ \hline Nanchang & Highway & Wuhan \\ Wuhan & Railway & Zhengzhou \\ Zhengzhou & Railway & Chongqing \\ Chongqing & Railway & Huaihua \\ Huaihua & Railway & Guyang \\ Guiyang & Railway & Nanning \\ Nanning & Railway & Beihai \\ Beihai & Highway & Kunming \\ Kunming & Highway & Guangzhou \\ Guangzhou & Railway & Shanghai \\ Shanghai & Railway & Manchouil \\ Manchouli & Highway & Erenhot \\ Erenhot & Railway & Alataw pass \\ Alataw pass & Railway & Kashi \\ Kasli & Railway & Gwadar \\ Guvadar & Highway & Yangon \\ Yongon & Railway & Bangkok \\ Bangkok & Railway & Ho Chi Minh \\ Ho Chi Minh & Railway & Singapore \\ Singapore & Highway & Rotterdam \\ Rotterdam & Highway & Hamburg \\ Hamburg & Railway & Warsaw \\ Warsaw & Highway & Duisburg \\ Duisburg & Highway & Berlin\({}^{\blacksquare}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimizing the optimal path of multimodal transport by the AGA algorithm. Manzhouli to Erenhot, from Erlianhot to Alashankou, and from Singapore to Rotterdam, and finally, highway transportation is used from Warsaw to Duisburg. Other modes of transportation are railway transportation. Figures 20-22 show the convergence curves of the iterative process of the three target values for the optimization of the FAGA algorithm, from which it can be seen that the cost target reaches its optimum at about 260 iterations of CNY 102,000. Similarly, the time target converges to its optimum at about 290 iterations of 630 h, and the carbon emission target reaches its optimum at about 300 iterations of 60,100 kg. Figure 23 shows the fitness curves of the ordinary GA, AGA, EGA, and FAGA. By comparing the figures, it can be concluded that the fitness function of the FAGA algorithm converges in 6713 iteration over 150 generations, while the GA algorithm converges in 6755 iteration over 325 generations. The EGA algorithm converges in 6730 iterations over 200 generations, and the AGA algorithm converges in 6730 iterations over 160 generations. It can be seen that the FAGA algorithm has a fast convergence speed, good convergence effect, and good optimization quality.