## 7 Experiments and Results Analysis
### Parameter Analysis
#### 7.2.1 The Analysis of SN  
Set _Limit_ = 30, _Cycle_ = 50. When the \(SN\) is 20, 30, 40, respectively, the fitness of the path generated by the OEABC is shown in Figure 5. As can be seen from Figure 5, When \(SN\) = 20, in the 30 paths generated by the algorithm, a total of four paths have a fitness change greater than 10% of the average fitness, compared with the generated paths, the ratio of paths greater than 10% of the average fitness is 13%. This is because the initial paths generated by OEABC are stochastic, and when the number of iteration _Cycle_ is greater than the limit, the new path generated by the scout bee is also stochastic.  
When \(SN\) = 30, there are three points with an amplitude of change greater than 10% of the average fitness, and compared with the generated paths, only 6% of the paths are more than 10% of the average fitness, and the variation amplitude of the generated solution tends to converge with the increase of \(SN\).  
When \(SN\) = 40, there is only one point with a variation amplitude greater than 10% of the average fitness, and compared with the generated paths, only 3% of the paths are more than 10% of the average fitness, the fluctuation range of the fitness curve becomes smaller and tends to be stable.  
Therefore, the bigger the \(SN\) is, the higher the fitness of the path generated by the algorithm, the closer we are to the optimal solution. At the same time, it can also be seen that the path ratio of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & \multicolumn{3}{c}{**Path**} & \(\eta\) & \(C\) & \(E\) & **Fitness** \\ \hline
1 & \(\
u_{0}\), \(\
u_{6}\), \(\
u_{19}\), \(\
u_{35}\), \(\
u_{53}\), \(\
u_{67}\), \(\
u_{58}\), \(\
u_{100}\), \(\
u_{112}\), \(\
u_{133}\), \(\
u_{140}\), \(\
u_{150}\) & 0.69 & 0.51 & 21 & 56.21 \\
2 & \(\
u_{0}\), \(\
u_{5}\), \(\
u_{28}\), \(\
u_{37}\), \(\
u_{60}\), \(\
u_{75}\), \(\
u_{84}\), \(\
u_{100}\), \(\
u_{118}\), \(\
u_{132}\), \(\
u_{138}\), \(\
u_{150}\) & 0.51 & 0.42 & 27 & 52.01 \\
3 & \(\
u_{0}\), \(\
u_{13}\), \(\
u_{28}\), \(\
u_{35}\), \(\
u_{49}\), \(\
u_{71}\), \(\
u_{81}\), \(\
u_{107}\), \(\
u_{123}\), \(\
u_{136}\), \(\
u_{150}\) & 0.75 & 0.31 & 17 & 71.46 \\
4 & \(\
u_{0}\), \(\
u_{10}\), \(\
u_{23}\), \(\
u_{43}\), \(\
u_{48}\), \(\
u_{74}\), \(\
u_{75}\), \(\
u_{85}\), \(\
u_{93}\), \(\
u_{114}\), \(\
u_{139}\), \(\
u_{150}\) & 0.73 & 0.33 & 18 & 68.60 \\
5 & \(\
u_{0}\), \(\
u_{7}\), \(\
u_{27}\), \(\
u_{41}\), \(\
u_{56}\), \(\
u_{73}\), \(\
u_{90}\), \(\
u_{93}\), \(\
u_{109}\), \(\
u_{131}\), \(\
u_{149}\), \(\
u_{150}\) & 0.54 & 0.39 & 17 & 54.34 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results after 50 iterations.  
Figure 5: The fitness of the path when the \(SN\) is different.  
fitness change amplitude greater than 10% of the average fitness decreases from 13% to 3%. Therefore, with the increase of \(SN\), the variation amplitude of the generated solution tends to converge, and the stability of the algorithm is gradually improved. This is because \(SN\) becomes larger, which means that the larger the solution space is, the higher the probability of obtaining the optimal solution is. However, the increase of \(SN\) will lead to the synchronous increase of the overhead of the algorithm, which cannot increase the \(SN\) indefinitely. It is necessary to select the \(SN\) which is suitable for the scale of the problem.