[1]赵亚南,张良,许佳明.基于多目标人工蜂鸟算法的输水泵站优化调度[J].江苏水利,2026,(06):40-45.
　ZHAO Yanan,ZHANG Liang,XU Jiaming.Optimal scheduling of water pumping stations based on multi-objective artificial hummingbird algorithm[J].JIANGSU WATER RESOURCES,2026,(06):40-45.
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基于多目标人工蜂鸟算法的输水泵站优化调度()

《江苏水利》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2026年06期
页码:
40-45
栏目:
水利信息化
出版日期:
2026-07-02

文章信息/Info

Title:
Optimal scheduling of water pumping stations based on multi-objective artificial hummingbird algorithm
文章编号:
1007-7839（2026）06-0040-0006
作者:
赵亚南1; 张良2; 许佳明2
（1.徐州市水利工程运行管理中心，江苏 徐州 221000；2.江苏省工程勘测研究院有限责任公司，江苏 扬州 225100）
Author(s):
ZHAO Yanan1;  ZHANG Liang2;  XU Jiaming2
（1.Xuzhou Water Conservancy Project Operation Management Center, Xuzhou 221000, China；2.Jiangsu Province Engineering Investigation and Research Institute Co., Ltd., Yangzhou 225100, China）
关键词:
输水泵站; 多目标优化; 人工蜂鸟算法; 运行效率; 总能耗
Keywords:
water pumping station;  multi-objective optimization;  artificial hummingbird algorithm;  operational efficiency;  total energy consumption
分类号:
TV675
文献标志码:
A
摘要:
针对泵站传统调度方法难以协同优化运行效率与能耗的问题，以郑集水利枢纽为研究对象，构建了以运行效率最高与总能耗最低为目标的泵站多目标优化调度模型，并引入多目标人工蜂鸟算法（MOAHA）进行求解。结果表明：相较于常规经验调度，MOAHA优化方案使郑集东站在35、47、53 m3/s工况下的总运行效率平均提升约1.0%，郑集西站在不同工况下效率亦得到稳定改进；与遗传算法（GA）和粒子群算法（PSO）相比，MOAHA在复杂工况下具有更快的收敛速度和更优的寻优精度，在东站53 m3/s工况中，其优化方案日能耗较GA和PSO分别降低609 kWh与516 kWh。该研究为输水泵站的精细化、智能化调度提供了有效的模型方法与决策支持。
Abstract:
To address the challenge of coordinating operational efficiency and energy consumption in traditional pump station scheduling methods, this study takes the Zhengji Water Control Project as the research subject and establishes a multi-objective optimization scheduling model for pump stations aimed at maximizing operational efficiency and minimizing total energy consumption. The Multi-Objective Artificial Hummingbird Algorithm （MOAHA） is introduced for solution. The results show that compared to conventional empirical scheduling, the MOAHA-optimized scheme increases the overall operational efficiency of Zhengji East Station by approximately 1.8% under flow rates of 35, 47, and 53 m3/s, while also achieving stable efficiency improvements at Zhengji West Station across various conditions. When compared to Genetic Algorithm （GA） and Particle Swarm Optimization （PSO）, MOAHA demonstrates faster convergence and superior optimization accuracy under complex conditions. At the 53 m3/s flow rate for East Station, its optimized solution reduces daily energy consumption by 609 kWh and 516 kWh compared to GA and PSO, respectively. This research provides effective model methods and decision support for refined and intelligent scheduling of water transfer pump stations.

参考文献/References:

[1]蓝旭坤，杨云，朱麟. 用地紧缺地区输水泵站建设关键问题及解决措施[J]. 工程技术研究，2025，10（21）：149-151.
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备注/Memo

备注/Memo:
收稿日期：2026-02-02
作者简介：赵亚南（1983—），男，工程师，本科，主要从事水利水电工程工作。E-mail:hou62228@163.com
更新日期/Last Update: 2026-06-01