Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation
Abstract Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmeti...
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80419-w |
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| author | Qinglai Xiong Ling Dong Hu Chen Xueping Zhu Xuehua Zhao Xuerui Gao |
| author_facet | Qinglai Xiong Ling Dong Hu Chen Xueping Zhu Xuehua Zhao Xuerui Gao |
| author_sort | Qinglai Xiong |
| collection | DOAJ |
| description | Abstract Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions. Considering the advantages of both operators, this study introduces an improved arithmetic operator to reduce the risk of local convergence inherent in conventional arithmetic operators. Subsequently, two strategies for the SBAX operator are discussed: SBX operator + new arithmetic operator and new arithmetic operator + SBX operator. The convergence of the bi-objective Pareto solution set is evaluated based on the generation and inverted generational distances. This method is used for the collaborative optimisation of the water supply and ecological operation of the Fenhe Reservoir, where its effectiveness is demonstrated. A comparative analysis of the bi-objective optimisation schemes obtained using different crossover operators indicates the following: (1) the NSGA-II algorithm based on the SBAX operator achieves a convergence efficiency that is 14.25–41.95% higher than that of the conventional NSGA-II algorithm; (2) the reservoir operation indices of the scheduling scheme derived from the NSGA-II algorithm based on the SBAX operator significantly outperform those obtained using the conventional NSGA-II algorithm. The optimal strategy reduces the annual average water abandonment by 11.2–14.52 million m3. This study provides a novel approach for bi-objective optimisation and sustainable reservoir management. |
| format | Article |
| id | doaj-art-511533a2207641ebace77ba1bb0ee8b1 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-511533a2207641ebace77ba1bb0ee8b12025-08-20T02:53:46ZengNature PortfolioScientific Reports2045-23222024-12-0114112010.1038/s41598-024-80419-wEnhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operationQinglai Xiong0Ling Dong1Hu Chen2Xueping Zhu3Xuehua Zhao4Xuerui Gao5College of Water Resources Science and Engineering, Taiyuan University of TechnologyCollege of Water Resources Science and Engineering, Taiyuan University of TechnologyCollege of Water Resources Science and Engineering, Taiyuan University of TechnologyCollege of Water Resources Science and Engineering, Taiyuan University of TechnologyCollege of Water Resources Science and Engineering, Taiyuan University of TechnologyInstitute of Soil and Water Conservation, Northwest A&F UniversityAbstract Reservoir-operation optimisation is a crucial aspect of water-resource development and sustainable water process management. This study addresses bi-objective optimisation problems by proposing a novel crossover evolution operator, known as the hybrid simulated binary and improved arithmetic crossover (SBAX) operator, based on the simulated binary cross (SBX) and arithmetic crossover operators, and applies it to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) algorithm to improve the algorithm. In particular, the arithmetic crossover operator can obtain an optimal solution more precisely within the solution space, whereas the SBX operator can explore a broader range of potential high-quality solutions. Considering the advantages of both operators, this study introduces an improved arithmetic operator to reduce the risk of local convergence inherent in conventional arithmetic operators. Subsequently, two strategies for the SBAX operator are discussed: SBX operator + new arithmetic operator and new arithmetic operator + SBX operator. The convergence of the bi-objective Pareto solution set is evaluated based on the generation and inverted generational distances. This method is used for the collaborative optimisation of the water supply and ecological operation of the Fenhe Reservoir, where its effectiveness is demonstrated. A comparative analysis of the bi-objective optimisation schemes obtained using different crossover operators indicates the following: (1) the NSGA-II algorithm based on the SBAX operator achieves a convergence efficiency that is 14.25–41.95% higher than that of the conventional NSGA-II algorithm; (2) the reservoir operation indices of the scheduling scheme derived from the NSGA-II algorithm based on the SBAX operator significantly outperform those obtained using the conventional NSGA-II algorithm. The optimal strategy reduces the annual average water abandonment by 11.2–14.52 million m3. This study provides a novel approach for bi-objective optimisation and sustainable reservoir management.https://doi.org/10.1038/s41598-024-80419-wReservoir operationBi-objective optimisationNSGA-IICrossover operatorSBAX operator |
| spellingShingle | Qinglai Xiong Ling Dong Hu Chen Xueping Zhu Xuehua Zhao Xuerui Gao Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation Scientific Reports Reservoir operation Bi-objective optimisation NSGA-II Crossover operator SBAX operator |
| title | Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation |
| title_full | Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation |
| title_fullStr | Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation |
| title_full_unstemmed | Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation |
| title_short | Enhanced NSGA-II algorithm based on novel hybrid crossover operator to optimise water supply and ecology of Fenhe reservoir operation |
| title_sort | enhanced nsga ii algorithm based on novel hybrid crossover operator to optimise water supply and ecology of fenhe reservoir operation |
| topic | Reservoir operation Bi-objective optimisation NSGA-II Crossover operator SBAX operator |
| url | https://doi.org/10.1038/s41598-024-80419-w |
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