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中文摘要：
针对传统优化方法在求解高维、复杂的梯级水电站短期发电优化调度时易出现“维数灾”或陷入局部最优解的缺陷，提出了免疫蛙跳算法（ISFLA）。ISFLA将克隆选择算法嵌入到混洗蛙跳算法框架中，对混合之后的蛙群构造子群体执行免疫克隆选择操作，同时使用改进的最差解更新方式以提高其局部搜索能力，进而将其应用于某梯级水电站短期发电优化调度中。通过将ISFLA与标准混洗蛙跳算法、粒子群算法以及逐步优化方法对比，优化结果表明ISFLA在求解梯级水电站短期发电优化问题时具有有效性和优越性。
英文摘要：
Traditional optimization methods, when used to solve the mathematically complex and high-dimensional problem in optimal operation of a cascade hydropower station system, often encounter dimension disaster and trapping into a local optimum. This paper presents an immune-shuffied frog-leaping algorithm （ISFLA） that adopts a clone-selecting algorithm in population space in the SFLA framework. ISFLA constructs subgroup shuffling and executes operations of cloning and selecting, meanwhile it adopts an advanced step-updating policy to improve local search ability. It has been applied to optimized operation of cascade hydropower stations for short-term power generation. Results show that ISFLA has better efficiency and superiority to standard SFLA, particle swam optimization, or progressive optimization algorithm.
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