Erratum to “A modified multi-objective grey wolf optimizer for multi-objective flood control operation of cascade reservoirs” [J. Hydrol. 658 (2025) 133162] (Journal of Hydrology (2025) 658, (S0022169425005001), (10.1016/j.jhydrol.2025.133162))

This paper was recently published in the Journal of Hydrology (Liu et al., 2025). However, Section 6: Conclusion contains a missing paragraph. The conclusion in the final submitted version on the Journal of Hydrology's Editorial Manager was complete (see Editorial Manager), but the missing content occurred during the publication process. This omission happened during the proof stage. Unfortunately, we did not detect it because it was neither marked as deleted nor annotated for correction during the proofing process (see Proof Central). After the paper was published online, we noticed the omission, and therefore we proposed this erratum. The currently published, incomplete conclusion is as follows: The RFCO problem is a complex multi-objective optimization challenge, particularly during the operation of long-duration floods involving cascade reservoirs, which significantly increases the decision variables and poses a considerable challenge to algorithms. The correct and complete conclusion should be: The RFCO problem is a complex multi-objective optimization challenge, particularly during the operation of long-duration floods involving cascade reservoirs, which significantly increases the decision variables and poses a considerable challenge to algorithms. Inspired by the hunting behavior of real-world wolf packs, the spiral hunting model and sudden leap model have been developed and integrated along with other improvement strategies into MOGWO, resulting in the creation of MMOGWO. The algorithm can handle high-dimensional variable problems, and is therefore applied to a RFCO problem. The main conclusions of this study are drawn as follows: (1) The performance of MMOGWO was validated on benchmark functions UF8–UF10, DTLZ2, and DTLZ7, and compared with MOGWO and three competitive multi-objective optimization algorithms introduced in the past three years. Based on the statistical results of performance metrics and the distribution of the Pareto solution sets, MMOGWO has demonstrated excellent convergence and uniformity in its Pareto solutions, thus establishing it as a competitive multi-objective optimization algorithm.(2) MMOGWO can be considered a reliable algorithm for flood control operations of cascade reservoirs. Its performance does not diminish with an increase in decision variables. The solutions provided by MMOGWO are not only diverse but also rational, as confirmed through the analysis of both the trade-off between objectives and the operation schemes generated from characteristic solutions.