关键词: 梯级水库, 优化调度, 混合蛙跳算法, 局部精细搜索, 全局激励调节, 耦合改进机制

Abstract: In view of the premature convergence in shuffled frog leaping algorithm (SFLA), an improved shuffled frog leaping algorithm (AISFLA) is proposed by coupling the local refine search strategy (LRSS) with the global incentive regulation strategy (GIRS). LRSS improves the local search ability by using chaos technology to conduct more refined search around the optimal individual of each group, while GIRS keeps an efficient global search performance by disturbing the optimum individual to improve the frogs’ population diversity and further motivate frogs jumping out of stable state. AISFLA is applied to the optimal operation of Lixianjiang cascade reservoirs as a demonstration. The modeling result proves that AISFLA is of high optimization quality and fast convergence by effectively handling the premature convergence of SFLA, hence can be a new approach to the solution of optimal operation of cascade reservoirs.

Key words: cascade reservoirs, optimal operation, shuffled frog leaping algorithm, local refine search strategy, global incentive regulation strategy, coupling improvement mechanism