基于多目标聚类分解的水库防洪优化调度OA

To address the problem of decision-making space constraints in multi-objective optimization for reservoir flood control,this study proposes a collaborative decision-making framework that fuses an improved NSGA-Ⅱ algorithm with multi-objective cluster decomposition.The Pareto frontier solution is clustered and decomposed to provide optimal scheduling decisions with different decision preferences.The application of the proposed method to A Reservoir yielded the following conclusions:①Multi-objective optimization can significantly improve the flood control effect of A Reservoir.The Pareto frontier solution of flood control optimization scheduling can reduce the downstream peak flood flow,the duration of high flows,and the flood high water level by 33.4%,12.7%,and 1.32 m,respectively.② Considering competition among multiple objectives,multi-objective clustering decomposition offers more targeted decision-making guidance:prioritizing reservoir flood control safety cuts the flood regulation high water level by 1.5 m;prioritizing downstream flood control safety reduces downstream flood peak discharge by 25.3%.