Optimization of Distribution Network Reconfiguration using Various Algorithms for Power Loss Minimization

Optimal reconfiguration of distribution networks represents a critical problem in power systems aimed at minimizing power losses while ensuring network reliability and stability. This study evaluates the performance of three optimization algorithms: Archimedes Optimization Algorithm (AOA), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA), in solving the distribution network reconfiguration (DNR) problem. The algorithms were tested and AOA outperformed PSO and GA, achieving power loss reductions of $\mathbf{3 4. 4 2 \%}, \mathbf{6 5. 4 0 \%}$, and $\mathbf{3 5. 8 8 \%}$ in the 7-bus, 11-bus, and 16-bus systems, respectively, with faster computation times. The results demonstrate that the AOA consistently achieves superior performance compared to PSO and GA, with significant reductions in power losses and improved voltage profiles. These findings highlight the robustness and efficiency of the AOA, making it a promising tool for optimizing distribution networks.