基于免疫蛙跳算法的梯级水库群优化调度

针对混洗蛙跳算法存在的问题,结合克隆选择算法和混洗蛙跳算法各自优势,提出了一种免疫蛙跳算法(ISFLA),并将其应用于梯级水库群优化调度中. ISFLA将克隆选择算法嵌入到混洗蛙跳算法框架中,对整个群体循环进行分组进化与混合,在混合之后构造子群体执行克隆选择操作, 以提高算法的局部搜索能力.通过实际工程验证了该算法的可行性与高效性,从而为梯级水库群发电调度问题的求解提供了一种新的途径.

Optimal operation of cascade reservoirs based on immune-shuffled frog leaping algorithm

To improve the optimization capability of traditional shuffled frog leaping algorithm (SFLA), a new immune-shuffled frog leaping algorithm (ISFLA) by hybridizing clone selecting algorithm and shuffled frog leaping algorithm is proposed and used for optimal scheduling of cascade reservoirs. In the proposed algorithm, the total population periodically executes grouping and shuffling operations, and after shuffling the subgroup is constructed and executed cloning and selecting operations to improve the local search ability. Finally, the proposed algorithm is verified through practical example, and the result shows the algorithm is feasible and fast efficient, which provides a new approach to solve optimal scheduling of cascade reservoirs.