To address the limitations of the dung beetle optimization (DBO) algorithm in shipboard power system fault reconfiguration, including its susceptibility to local optima, slow convergence, and inability to directly handle discrete variables, an improved DBO (IDBO) algorithm-based fault reconfiguration method is proposed.

First, the initial population is optimized by introducing the Tent chaotic mapping, and the adjustment mechanism of the convergence factor R is improved to avoid the risk of the algorithm falling into local optima. Then, the migration strategy of the beluga whale optimization (BWO) algorithm is incorporated and a mutation mechanism is introduced to accelerate convergence and enhance global search capability. Finally, a discrete encoding strategy is introduced to map the continuous algorithm onto the discrete decision space of the shipboard power system.

Simulation results demonstrate that, compared with the DBO algorithm, particle swarm optimization, genetic algorithm, adjustable space operator discrete state transition algorithm, and multifactorial evolutionary algorithm, the IDBO algorithm exhibits superior performance in both branch fault and generator fault scenarios, providing more accurate shipboard power system reconfiguration solutions with faster convergence. For single generator fault scenarios, the optimal solution is obtained by IDBO within only 4 iterations, while for branch fault scenarios, the optimal solution is obtained within just 3 iterations.

The system reconfiguration scheme is rapidly and effectively generated by IDBO, significantly enhancing the safety and stability of the shipboard power system.