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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.
Aiming at the existing problems of shuffled frog leaping algorithm and combining the respective advantages of clonal selection algorithm and shuffled frog leaping algorithm, an immune leapfrogging algorithm (ISFLA) is proposed and applied to the optimal operation of cascade reservoirs. ISFLA cloned The selection algorithm is embedded into the shuffled frog leaping algorithm framework, and the entire group cycle is evolved into a group and mixed. After mixing, subgroups are constructed to perform clonal selection operations to improve the local search ability of the algorithm. The feasibility of the algorithm is verified through practical engineering Sex and efficiency, so as to provide a new way to solve the problem of power generation scheduling in cascade reservoirs.