Abstract: In solving the joint scheduling problem of cascade hydropower stations, the standard quantum-behaved particle swarm optimization (QPSO) algorithm suffers from premature convergence and local trapping, among other shortcomings. This paper presents a hybrid QPSO (HQPSO) that combines the advantages of the two-fold improvement strategy. This new method first does mutation search for individual extremes at a given probability to increase the diversity of individuals and enhance the global exploiting capability of the population. Then, it establishes an external archive set to conserve certain particles found in the evolutionary process. Finally, it uses the Nelder-Mead operator for dynamic probability identification to help particles searching in the neighborhood, improving its searching capability and avoiding falling into a local optimum. Application to the Wu River shows that the HQPSO is faster in convergence and global searching and practically applicable, avoiding the shortcomings of QPSO.