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In all-electric navy ships, severe damage or faults may occur during battle conditions. This might even affect the generators, and as a result, critical loads might suffer from power deficiencies for a long time, ultimately leading to a complete system collapse. A fast reconfiguration of the power path is therefore necessary in order to serve the critical loads and to maintain a proper power balance in the ship's power system. A fast intelligent reconfiguration algorithm based on small-population-based particle swarm optimization (PSO) (SPPSO) is presented in this paper. The reconfiguration of the electric ship's power system is formulated as a single objective as well as a multiobjective optimization problem. In the case of multiobjective optimization, the Pareto optimal solutions are obtained by SPPSO from two conflicting objective functions. From the Pareto set, the final solution is chosen depending on users' preferences regarding the mission of the navy ship. SPPSO is a variant of PSO having fewer numbers of particles and regenerating new solutions within the search space every few iterations. This concept of regeneration in SPPSO makes the algorithm fast and greatly enhances its capability. The strength of the proposed reconfiguration strategy is demonstrated on a real-time digital simulator environment.