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This paper presents a compensation algorithm based on a Multi-Objective Genetic Algorithm (MOGA) for recovering beam parameters in high-intensity superconducting proton linacs when one or more radio-frequency cavities degrade or fail. The algorithm directly adjusts the amplitude and phase of adjacent or all cavities to restore the beam energy and energy spread at the linac exit. Compared with lookup-table methods, the MOGA-based approach offers greater flexibility in handling complex failure scenarios, including partial gradient loss and multi-cavity faults. Although computationally more intensive, the method has been successfully tested in beam dynamic study for the CSNS-II superconducting linac and experimentally verified on the C-ADS Injector I, demonstrating its capability to meet strict beam stability requirements for downstream injection. This work provides a robust and adaptive strategy for fault-tolerant operation in high-power proton accelerator facilities.
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