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For the complex problem of cascade hydropower system optimal scheduling, a novel grouping differential evolution algorithm (GDE) is proposed in this paper by hybridizing differential evolution (DE) and shuffled frog leaping (SFL). In the proposed algorithm, the population is periodically executes grouping and shuffling operations, and the individuals are updated according to differential evolution in each memplex. Finally, the algorithm is applied to a case of cascade hydropower system mid-long term optimal scheduling. The results show its feasible and more efficient then dynamic programming. Thus it can be provided as an effective alternative for solving the complex hydropower system optimization problems.