## 5 Simulation Results
### Performance Analysis of the Optimization Algorithm  
This paper employs the ITLCO algorithm to optimize the objective function of the UDN, incorporating the VPP reconfiguration model. Comparative analysis is conducted using the optimization results from the TLCO, IFA, ISOA, and ALO algorithms. The parameter settings for the algorithms are shown in Table 5.  
Simulations were conducted in the MATLAB environment, and the convergence curves for the ITLCO, TLCO, IFA, ISOA, and ALO algorithms are presented in Figure 4. The parameter comparisons for these four algorithms when reaching the minimum fitness value are detailed in Table 6. By examining Figure 4 and Table 6, it is evident that the ITLCO algorithm achieves a convergence state with a fitness value of 3.064 \(\times\) 10\({}^{-3}\) after only 39 iterations, with a total computation time of 1.49 s. When contrasted with the other four algorithms, the ITLCO algorithm demonstrates a notably shorter computation time and a higher degree of convergence accuracy. This outcome effectively validates the substantial enhancement in the performance of the TLCO algorithm, which is achieved through the implementation of the chaotic mapping initialization strategy and the incremental pheromone update model. This not only showcases the superiority of the ITLCO algorithm in computational efficiency and convergence quality but also provides strong evidence for \begin{table}
\begin{tabular}{c c} \hline \hline
**Loop** & **Switch Number** \\ \hline
1 & (2)(3)(4)(5)(6)(18)(19)(33) \\
2 & (7)(8)(9)(20)(21)(33)(35) \\
3 & (3)(4)(5)(22)(23)(24)(25)(26)(27)(28)(37) \\
4 & (6)(7)(8)(15)(16)(17)(25)(26)(27)(28)(29)(30)(31)(32)(36) \\
5 & (8)(9)(10)(11)(12)(34) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fundamental loop parameters of the UDN. Figure 3: An improved IEEE 33-bus system. the effectiveness of the proposed improvement methods in optimizing the performance of the TLCO algorithm. It can be observed from Table 7 that there are differences in the reconfiguration results of the UDN incorporating VPP obtained by different algorithms. Specifically, the voltage deviation of the reconfigured UDN incorporating VPP using the ITLCO algorithm is 0.2370 p.u., which is reduced by 4.09%, 6.12%, 8.07%, and 14.59%, respectively, compared with the reconfiguration results of the other four algorithms. Similarly, the system power loss of the reconfigured UDN incorporating VPP using the ITLCO algorithm is 163.59 kW, which is decreased by 6.65%, 20.59%, 29.18%, and 37.52%, respectively, compared with the reconfiguration results of the other four algorithms. These data indicate that the UDN incorporating VPP reconfigured using the ITLCO algorithm performs better regarding stability and economy, thereby verifying the practicality and effectiveness of the proposed reconfiguration model and its solution method. \begin{table}
\begin{tabular}{c c} \hline \hline
**ITLCO (TLCO)** & \\ \hline Parameter Name & Value \\ Maximum Number of Iterations & 300 \\ Iteration Count & 300 \\ Termite Population Size & 100 \\ Dimension of Search Space & 108 \\ \hline \hline IFA & \\ \hline Parameter Name & Value \\ Maximum Number of Iterations & 300 \\ Iteration Count & 300 \\ Number of Fireworks & 100 \\ Explosion Coefficient & 0.75 \\ Explosion Strength & 0.05 \\ Explosion Range & 2 \\ \hline \hline ISOA & \\ \hline Parameter Name & Value \\ Maximum Number of Iterations & 300 \\ Iteration Count & 300 \\ Population Size & 100 \\ Spiral Movement Coefficient 1 & 1 \\ Spiral Movement Coefficient 2 & 0.1 \\ Control Coefficient & Linearly decreasing from 2 to 0 \\ \hline \hline ALO & \\ \hline Parameter Name & Value \\ Maximum Number of Iterations & 300 \\ Iteration Count & 300 \\ Population Size & 100 \\ Ant Random Walk Step Length Scaling & 0.7 \\ Antlion Influence Factor & 3 \\ Elitism Ratio & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fundamental loop parameters of the UDN incorporating VPP.