Optimization of short-term operation of cascade hydropower stations using normal cloud mutation shuffled frog leaping algorithm

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.