基于仿水循环算法的梯级水库群多目标优化调度研究  

Multi-objective Optimal Dispatching of Cascade Reservoirs Based on Water Cycle-like Algorithm

作　　者：吴智丁 WU Zhiding(Datang Hydropower Science&Technology Research Institute Co.,Ltd.,Nanning 530025,Guangxi,China)

机构地区：[1]大唐水电科学技术研究院有限公司

出　　处：《水力发电》2019年第11期101-107,共7页Water Power

摘　　要：仿水循环算法(WCA)是一种新的智能种群优化算法,将该方法引入梯级水库群多目标优化调度,并提出多目标仿水循环算法(MWCA)。MWCA通过对自然界水循环过程的模拟,构建多目标下的相对重力机制,实现对非劣解的有效搜索,建立汇流、分流、渗流、蒸发降雨4个搜索策略,提升算法的收敛速度、多样性和局部搜索能力,同时有效克服传统算法的早熟问题,令算法具有较强的全局搜索和收敛性能。在梯级水库群多目标优化调度实例计算中,与多目标粒子群算法MOPSO和多目标遗传算法NSGA-Ⅱ进行比较分析,结果表明MWCA在计算结果和非劣解多样性上均优于其他算法,为梯级水库群多目标优化调度问题提供了一种有效的求解思路。A new intelligent swarm-based optimization method called water cycle-like algorithm( WCA) is utilized to address the problem of multi-objective optimal dispatching for cascade reservoirs,and then the multi-objective water cycle-like algorithm( MWCA) is proposed. By simulating natural water cycle process,the MWCA constructs a relative gravity mechanism under multiple objective conditions to achieve an effective search for non-inferior solutions. By establishing four search strategies of confluence,diffluence,infiltration and evaporation-rainfall,the convergence speed,diversity and local search capability are improved,and at the same time,the premature problem of traditional algorithms can be effectively overcame,which makes the new algorithm with strong global search and convergence performance. The proposed method is verified through a case simulation of five cascade reservoirs system,and the results are compared with MOPSO and NSGA-Ⅱ algorithms. The results show that the MWCA is superior to above two algorithms in both calculation results and non-inferior solution diversity. It provides an effective solution for multi-objective optimal dispatching problem of cascade reservoirs.

关 键 词：多目标仿水循环算法 相对重力机制 梯级水库群 多目标优化调度 

分 类 号：TV697.12[水利工程—水利水电工程]