Research on multi-objective optimal allocation of regional water resources based on improved sparrow search algorithm

被引:8
|
作者
Yao, Zhiyuan [1 ]
Wang, Zhaocai [1 ]
Cui, Xuefei [2 ]
Zhao, Haifeng [1 ]
机构
[1] Shanghai Ocean Univ, Coll Informat, Shanghai 201306, Peoples R China
[2] Shanghai Ocean Univ, Coll Engn, Shanghai 201306, Peoples R China
关键词
Cauchy-Gauss mutation; Elite chaos reverse learning; Levy flight; multi-objective; sparrow search algorithm; water resource allocation; PARTICLE SWARM OPTIMIZATION; MODEL; DYNAMICS; SYSTEM; SCALE;
D O I
10.2166/hydro.2023.037
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Nowadays, the conflict between the supply and demand of water resources in many regions is becoming increasingly prominent. Scientific allocation of regional water resources has become the key to solving the contradiction. In this study, a regional multi-objective water resources optimization allocation model considering social, economic, and ecological objectives is established, and four improvement spots are introduced to the sparrow search algorithm (SSA) to form an improved sparrow search algorithm (ISSA). By testing nine benchmark functions including monotonic and multi-peaked, the search efficiency and average convergence results of ISSA are significantly enhanced compared with other intelligent algorithms. Meanwhile, this research uses Luanchuan County, Henan Province, China, as an example to solve the water resource allocation scheme for 2025 and 2030 in the region using ISSA. The results show that the overall water shortage rate decreases to 3.49 and 2.79%, respectively, under the 75% guarantee rate, resulting in an effective reduction in future water shortages. Simultaneously, the scheme proposed has sound comprehensive benefits and can provide important technical support for the refined management of water resources, which is a reference and guidance for solving the contradiction between water supply and demand at the current stage.
引用
收藏
页码:1413 / 1437
页数:25
相关论文
共 50 条