A parallel multi-objective optimization based on adaptive surrogate model for combined operation of multiple hydraulic facilities in water diversion project

IF 2.2 3区 工程技术 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Hydroinformatics Pub Date : 2024-06-01 DOI:10.2166/hydro.2024.285
Xiaolian Liu, Zirong Liu, Xiaopeng Hou, Yu Tian, Xueni Wang, Leike Zhang, Hao Wang
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In a complex pressurized water diversion project (WDP), the combined optimal operation of multiple hydraulic facilities is computationally expensive owing to the requirement of massive mathematical simulation model runs. A parallel multi-objective optimization based on adaptive surrogate model (PMO-ASMO) was proposed in this study to alleviate the computational burden while maintaining its effectiveness. At the simulation level, an adaptive surrogate model was established, while a parallel non-dominated sorting genetic algorithm II (PNSGA-II) was utilized at the optimization level. Taking the successive shutdown of pumps as the operating process, the PMO-ASMO was applied to a complex pressurized diversion section of the Jiaodong WDP in China, and the results were compared with those obtained by NSGA-II and PNSGA-II. The results showed that the time consumption of PMO-ASMO was only 9.97% of that acquired by NSGA-II, which was comparable to that of PNSGA-II, in the case of 10-core parallelism. Moreover, compared with PNSGA-II, PMO-ASMO could find the optimal and stable Pareto front with the same number of simulation model runs, or even fewer runs. These results validated the effectiveness and efficiency of the PMO-ASMO. Therefore, the proposed framework based on multi-objective optimization is efficient for combined optimal operation of multiple hydraulic facilities.

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基于自适应代用模型的并行多目标优化,用于引水工程中多个水利设施的联合运行
查看 largeDownload 幻灯片查看 largeDownload 幻灯片 关闭模态在复杂的加压引水工程(WDP)中,由于需要运行大量数学模拟模型,因此多个水利设施的联合优化运行计算成本高昂。本研究提出了一种基于自适应代理模型的并行多目标优化方法(PMO-ASMO),以减轻计算负担,同时保持其有效性。在仿真层面,建立了自适应代用模型,而在优化层面则采用了并行非支配排序遗传算法 II(PNSGA-II)。以水泵连续停机为运行过程,将 PMO-ASMO 应用于中国胶东水电厂的一个复杂带压引水段,并将其结果与 NSGA-II 和 PNSGA-II 的结果进行了比较。结果表明,在 10 核并行的情况下,PMO-ASMO 的耗时仅为 NSGA-II 的 9.97%,与 PNSGA-II 的耗时相当。此外,与 PNSGA-II 相比,PMO-ASMO 可以在相同甚至更少的仿真模型运行次数下找到最优和稳定的帕累托前沿。这些结果验证了 PMO-ASMO 的有效性和效率。因此,所提出的基于多目标优化的框架可以有效地实现多个水利设施的联合优化运行。
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来源期刊
Journal of Hydroinformatics
Journal of Hydroinformatics 工程技术-工程:土木
CiteScore
4.80
自引率
3.70%
发文量
59
审稿时长
3 months
期刊介绍: Journal of Hydroinformatics is a peer-reviewed journal devoted to the application of information technology in the widest sense to problems of the aquatic environment. It promotes Hydroinformatics as a cross-disciplinary field of study, combining technological, human-sociological and more general environmental interests, including an ethical perspective.
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