<p>Multiobjective optimization algorithms have been widely proposed to increase hydropower generation and its associated benefits, such as water supply, navigation, and ecological protection, in multireservoir systems. However, the complexity arising from the numerous objectives and high-dimensional decision variables inherent in daily operations presents challenges for a single algorithm to effectively optimize reservoir operations. In this study, a multialgorithm fusion strategy was developed via efficient nondominated sorting to optimize the daily operation of a many-objective, high-dimensional multireservoir system. We assessed the performance of 48 widely utilized multiobjective algorithms and selected the six most effective algorithms for the Xiluodu–Xiangjiaba Cascade Reservoir system with four objective functions and 762 decision variables. We subsequently integrated the nondominated solutions from multiple algorithms and employed efficient nondominated sorting to extract the first Pareto front, thus establishing a multialgorithm fusion framework. The results demonstrated that compared with the six individual algorithms, the multialgorithm fusion strategy provided the greatest overall economic and ecological benefits. Specifically, compared with the algorithm with the best hydropower performance, the fusion strategy generates an average of 4.3 × 10⁸ kW·h more hydropower. It also achieved an average ecological flow alteration of 59.91% and a navigation satisfaction rate of 99.03%. Moreover, 16.41% of the operational schemes exceeded the water supply satisfaction rate by 92.88%. This approach offers a robust and effective solution for managing multireservoir systems under complex operational constraints and provides valuable insights into large-scale basin management.</p>