Efficient utilization of water resources in hydropower station operation is an important part of mitigating water and energy scarcity. Exploring efficient multi-objective optimization algorithms and studying the trade-off between water and energy have become the primary goal of multi-objective hydropower station optimal operation (MOHSOO). In this paper, a new improved multi-objective cuckoo search (IMOCS) algorithm is proposed to overcome the shortcomings of MOCS. Specifically, a population initialization strategy based on constraint transformation and the individual constraints and group constraints technique (ICGC) and a dynamic adaptive probability (DAP) are used to improve the search efficiency and the quality of solutions, respectively. A flock search strategy (FSS) is proposed to greatly speed up the convergence and improve the quality of the non-dominated solutions. In addition, the MOCS and NSGA-11 are presented as a comparison to test the performance of 1MOCS as well as three hybrids of MOCS combined with these strategies. An MOHS00 model of Xiaolangdi and Xixiayuan cascade hydropower stations in the lower Yellow River is built to verify the effectiveness of these algorithms together with five benchmark problems. The results show that 1MOCS performs better than other algorithms in convergence speed, convergence property, and diversity of solutions. For the Xiaolangdi hydropower station, there is a strong competitive relationship between power generation and water supply from September to next February, which severely restricts the power generation of the hydropower station. (C) 2018 Elsevier Ltd. All rights reserved.