Adaptive Dynamic Binary Whale Optimization Algorithm for Optimal Distribution Network Reconfiguration

This paper proposes an Adaptive Dynamic Binary Whale Optimization Algorithm (AD-BWOA) for solving the Distribution Network Reconfiguration (DNR) problem. The proposed method combines a nonlinear adaptation of the convergence parameter, a hybrid deterministic–stochastic mechanism for position-update selection, a binary transfer strategy, and a stagnation-based stopping criterion to improve the balance between exploration and exploitation and reduce unnecessary search iterations. The algorithm is implemented in Python and integrated with DIgSILENT PowerFactory, which performs the load-flow calculations and verifies the operational constraints of each candidate configuration. The performance of AD-BWOA is evaluated on 69-, 94-, and 135-bus distribution systems through 100 independent runs. The results show that the proposed method consistently reaches the best or reference solutions reported for the evaluated systems, with high repeatability in the 69- and 94-bus cases and competitive performance in the 135-bus system. In addition, the adaptive mechanisms contribute to reducing the number of iterations required for convergence under the adopted stopping criteria. The results indicate that AD-BWOA is a promising and reliable approach for discrete DNR problems, particularly when search-space reduction and adaptive control mechanisms are combined.