Xiong Qinglai, Dong Ling, Chen Hu, Zhu Xueping, Zhao Xuehua, Gao Xuerui
College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, Shaanxi, China.
Sci Rep. 2024 Dec 30;14(1):31621. doi: 10.1038/s41598-024-80419-w.
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 m. This study provides a novel approach for bi-objective optimisation and sustainable reservoir management.
水库调度优化是水资源开发和可持续水过程管理的关键环节。本研究提出一种新型交叉进化算子——混合模拟二进制与改进算术交叉（SBAX）算子，该算子基于模拟二进制交叉（SBX）算子和算术交叉算子，用于解决双目标优化问题，并将其应用于非支配排序遗传算法-II（NSGA-II）以改进该算法。具体而言，算术交叉算子能在解空间内更精确地获得最优解，而SBX算子能探索更广泛的潜在高质量解。考虑到两种算子的优势，本研究引入改进的算术算子以降低传统算术算子固有的局部收敛风险。随后，讨论了SBAX算子的两种策略：SBX算子+新算术算子和新算术算子+SBX算子。基于世代距离和倒置世代距离评估双目标帕累托解集的收敛性。该方法用于汾河水库供水与生态调度的协同优化，并验证了其有效性。对使用不同交叉算子获得的双目标优化方案进行比较分析，结果表明：（1）基于SBAX算子的NSGA-II算法收敛效率比传统NSGA-II算法高14.25%-41.95%；（2）基于SBAX算子的NSGA-II算法得出的调度方案的水库调度指标显著优于使用传统NSGA-II算法获得的指标。最优策略使年平均弃水量减少1120-1452万立方米。本研究为双目标优化和水库可持续管理提供了一种新方法。
