A dynamic multi-objective optimization model with interactivity and uncertainty for real-time reservoir flood control operation

被引:18
作者
Liu, Xiaolin [1 ]
Luo, Jungang [1 ]
机构
[1] Xian Univ Technol, State Key Lab Ecohydraul Northwest Arid Reg, Xian 710048, Shaanxi, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Reservoir flood control operation; Dynamic multi-objective optimization model; Interactivity; Evolutionary multi-objective optimization algorithm; EVOLUTIONARY ALGORITHM; MOEA/D; RULES;
D O I
10.1016/j.apm.2019.05.009
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Despite the successes of both multi-objective optimization and uncertainty handling techniques in reservoir flood control operation, no work has been done yet on developing and investigating dynamic multi-objective optimization models for this problem. In this work, a dynamic multi-objective optimization model with interactivity and uncertainty was developed for the real-time reservoir flood control operation. Accordingly, a dynamic multi-objective optimization algorithmic framework with two newly designed change reaction strategies was proposed for solving the proposed dynamic model. Following the proposed algorithmic framework, any evolutionary multi-objective optimization algorithm can be converted into a dynamic optimizer. After investigating the difficulty variation of the proposed dynamic model, the effectiveness and robustness of the proposed algorithmic framework have been validated based on experiential studies on two typical floods of Ankang reservoir. (C) 2019 Published by Elsevier Inc.
引用
收藏
页码:606 / 620
页数:15
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