中文摘要：

针对混合蛙跳算法（Shuffled Frog Leaping Algorithm,SFLA）早熟收敛的问题,利用云模型在知识表达时具有不确定中带有确定性、稳定之中又有变化的特点,对每个子族群的最优解进行正态云变异操作,调整青蛙的跳动步长以实现局部搜索,提出了一种云变异蛙跳算法（Normal Cloud Mutation SFLA,NCM-SFLA）,弥补混合蛙跳算法在进化过程中容易陷入局部最优的不足。将其应用于梯级水库的短期优化调度中,实例计算表明,与逐次逼近动态规划、混合蛙跳算法及标准粒子群算法相比,该方法具有更好的全局寻优能力和较快的收敛速度,验证了该方法在求解梯级水电站短期优化调度问题中的合理性和有效性。

英文摘要：

To improve the premature convergence problem of traditional shuffled frog leaping algorithm （SFLA）, normal cloud mutation operation is used to optimize the solution of each group by the cloud model＇s characteristics of uncertainty with certainty, stability, and flexibility in knowledge expression, and the beat step size is adjusted for local depth search. Using this technique, we have developed a normal cloud mutation-shuffled frog leaping algorithm （NCM-SFLA） that can avoid easy trapping into local optimum in calculation of evolution. This new algorithm was applied to short-term optimal dispatch of cascade hydropower stations. Application in a case study shows that it has better global search ability and faster convergence speed than those of DPSA, SFLA or PSO and it is effective in solution of short-term optimal operation of cascade hydropower stations.