Abstract: Objectives In order to enable the shipboard power system to recover rapidly from faults, an improved Dung Beetle Optimization (IDBO) algorithm-based fault reconfiguration method is proposed.Methods To address the issues of the Dung Beetle Optimizer (DBO) in ship power system fault reconfiguration, including susceptibility to local optima, slow convergence speed, and inconsistent data types, this study proposes the following improvements: Tent chaotic mapping is introduced to optimize the initial population while adjusting the convergence factor R to prevent local optima trapping; the Beluga Whale Optimization mechanism is integrated with a mutation strategy to enhance convergence efficiency; and a discrete encoding strategy is implemented to map the continuous algorithm into the discrete decision space of ship power systems. These modifications collectively improve solution quality, accelerate optimization speed, and ensure compatibility with practical engineering constraints. Results Simulation results demonstrate that, compared with the DBO, Particle Swarm Optimization, and Genetic Algorithm, the IDBO exhibits superior performance in both load fault and generator fault scenarios, achieving effective shipboard power system reconfiguration solutions with higher accuracy and faster convergence speed. For single generator faults, the optimal solution is obtained by IDBO within only 4 iterations, while for load faults, it is achieved in merely 3 iterations. Conclusions The system reconfiguration scheme is rapidly and effectively generated by IDBO, significantly enhancing the safety and stability of the shipboard power system.