彭勇

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:水利工程系

学科:水文学及水资源

办公地点:大连理工大学水利工程学院综合3#实验楼436

联系方式:电话:0411-84707911

电子邮箱:pengyong@dlut.edu.cn

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A Heuristic Dynamically Dimensioned Search with Sensitivity Information (HDDS-S) and Application to River Basin Management

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论文类型:期刊论文

发表时间:2015-05-01

发表刊物:WATER

收录刊物:SCIE、EI、Scopus

卷号:7

期号:5

页面范围:2214-2238

ISSN号:2073-4441

关键字:dynamically dimensioned search; non-uniform probability; dimensionality reduction; sensitivity analysis; Soil and Water Assessment Tool (SWAT); multi-reservoir operation

摘要:River basin simulation and multi-reservoir optimal operation have been critical for river basin management. Due to the intense interaction between human activities and river basin systems, the river basin model and multi-reservoir operation model are complicated with a large number of parameters. Therefore, fast and stable optimization algorithms are required for river basin management under the changing conditions of climate and current human activities. This study presents a new global optimization algorithm, named as heuristic dynamically dimensioned search with sensitivity information (HDDS-S), to effectively perform river basin simulation and multi-reservoir optimal operation during river basin management. The HDDS-S algorithm is built on the dynamically dimensioned search (DDS) algorithm; and has an improved computational efficiency while maintaining its search capacity compared to the original DDS algorithm. This is mainly due to the non-uniform probability assigned to each decision variable on the basis of its changing sensitivity to the optimization objectives during the adaptive change from global to local search with dimensionality reduced. This study evaluates the new algorithm by comparing its performance with the DDS algorithm on a river basin model calibration problem and a multi-reservoir optimal operation problem. The results obtained indicate that the HDDS-S algorithm outperforms the DDS algorithm in terms of search ability and computational efficiency in the two specific problems. In addition; similar to the DDS algorithm; the HDDS-S algorithm is easy to use as it does not require any parameter tuning and automatically adjusts its search to find good solutions given an available computational budget.