# Dynamic Reconfiguration of Active Distribution Network Based on Improved Equilibrium Optimizer
## 5 Case Study and Result
### Comparative Analysis of Different Algorithms  
To comprehensively evaluate the performance of the proposed Improved Equilibrium Optimizer (IEO) in solving the distribution network reconfiguration problem, this study conducts comparative experiments against several widely used metaheuristic algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Harris Hawk Optimization (HHO), the standard Equilibrium Optimizer (EO), and the Non-dominated Sorting Genetic Algorithm II (NSGA-II). To ensure fair and objective comparisons, all benchmark algorithms (including PSO, GWO, HHO, and NSGA-II) were executed under identical experimental settings. The population size, maximum number of iterations, and evaluation metrics were uniformly set across all methods. Moreover, for each algorithm, parameter settings were either selected based on widely accepted literature defaults or fine-tuned through preliminary experiments to ensure optimal performance without bias. All algorithms were independently run 30 times, and the best, worst values were recorded for performance analysis. This unified experimental configuration eliminates discrepancies in runtime or resource allocation, thereby validating the reliability and fairness of the comparative results. In order to provide a comprehensive assessment, this paper compares both the optimal and worst-case results for each algorithm, as shown in Table 3. The results reveal that, even in the worst-case scenario, the switch scheme produced by the improved EO algorithm outperforms the other algorithms in terms of network loss and minimum voltage. This paper also evaluates the performance of different algorithms by comparing their convergence behaviors and the number of iterations required by each algorithm, as shown in Figure 12. The convergence behavior reflects how many times the algorithm is called during the optimization process. As depicted in Figure 12, when using convergence behavior as the evaluation criterion, it is evident that the improved EO algorithm proposed in this study exhibits a shorter convergence time and a higher optimization rate compared to the other algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & \multicolumn{3}{c}{**Best Case**} & \multicolumn{3}{c}{**Worst Case**} \\ \cline{2-6}  & **Disconnected** & **Loss/kW** & **Minimum** & **Disconnected** & **Loss/kW** & **Minimum** \\  & **Branches** & **Voltage/p.u** & **Branches** & **Loss/kW** & **Voltage/p.u** \\ \hline PSO & 7-9-14-32-37 & 139.551 & 0.938 & 7-13-16-28-35 & 156.872 & 0.071 \\ GWO & 7-9-14-32-37 & 139.551 & 0.938 & 7-14-21-26-32 & 143.219 & 0.067 \\ HHO & 7-9-14-32-37 & 139.551 & 0.938 & 13-17-20-21-28 & 186.955 & 0.072 \\ NSGA-II & 7-9-14-32-37 & 139.551 & 0.938 & 7-10-15-21-26-32 & 149.328 & 0.069 \\ EO & 7-9-14-32-37 & 139.551 & 0.938 & 7-13-20-28-34 & 157.276 & 0.070 \\ Improved EO & 7-9-14-32-37 & 139.551 & 0.938 & 7-11-17-28-34 & 147.643 & 0.067 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of solution situations of different algorithms. To further validate the performance of the proposed Improved Equilibrium Optimizer (IEO), Table 3 provides a comparative analysis against several mainstream metaheuristic algorithms, including PSO, GWO, HHO, NSGA-II, and the standard EO. While all algorithms achieved identical results in the best-case scenario (minimum power loss of 139.551 kW and voltage of 0.938 p.u. ), differences became apparent under worst-case conditions. Among them, GWO recorded the lowest worst-case power loss (143.219 kW), while IEO demonstrated the best voltage stability (0.067 p.u.) and maintained consistently competitive loss values (147.643 kW). In addition, compared with GWO, which exhibits lower worst-case losses, IEO demonstrates superior performance on average. These results confirm that the IEO exhibits strong robustness and voltage control capability, especially under less favorable optimization runs.