改进NSGA-Ⅱ算法在水质监测点多目标优化研究中的应用

李云; 王志红; 王琦; 漆文光; 李斌; 吉瑞博; 龙志宏

doi:10.12052/gdutxb.170052

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

改进NSGA-Ⅱ算法在水质监测点多目标优化研究中的应用

更新时间：2026-05-27

- 改进NSGA-Ⅱ算法在水质监测点多目标优化研究中的应用
- A Study of Multi-objective Optimal Placement of Water Quality Monitoring Stations Based on Improved NSGA-Ⅱ Algorithm
- 广东工业大学学报 2018年35卷第2期页码：35-40
- 作者机构：
  
  1. 广东工业大学　土木与交通工程学院,广东,广州,510006
  2. 广州市自来水公司,广东,广州,510600
- 作者简介：
  
  李云(1992–)，男，硕士研究生，主要研究方向为供水管网水质监测点优化选址
  王志红(1975–)，女，教授，主要研究方向为供水管网优化理论及安全保障技术. E-mail: gdwzhihong@126.com
- 基金信息：
  
  广东省科技攻关项目(2014A020216044)
- DOI：10.12052/gdutxb.170052
  中图分类号： TU991
- 网络首发：2018-03-09，
  
  纸质出版：2018
- 稿件说明：
移动端阅览
李云, 王志红, 王琦, 漆文光, 李斌, 吉瑞博, 龙志宏. 改进NSGA-Ⅱ算法在水质监测点多目标优化研究中的应用[J]. 广东工业大学学报, 2018, 35(2): 35-40.

Li Yun, Wang Zhi-hong, Wang Qi, et al. A Study of Multi-objective Optimal Placement of Water Quality Monitoring Stations Based on Improved NSGA-Ⅱ Algorithm[J]. 2018, 35(2): 35-40.
李云, 王志红, 王琦, 漆文光, 李斌, 吉瑞博, 龙志宏. 改进NSGA-Ⅱ算法在水质监测点多目标优化研究中的应用[J]. 广东工业大学学报, 2018, 35(2): 35-40. DOI： 10.12052/gdutxb.170052.

Li Yun, Wang Zhi-hong, Wang Qi, et al. A Study of Multi-objective Optimal Placement of Water Quality Monitoring Stations Based on Improved NSGA-Ⅱ Algorithm[J]. 2018, 35(2): 35-40. DOI： 10.12052/gdutxb.170052.

摘要

为了改善NSGA-Ⅱ算法在水质监测点多目标优化选址的应用效果

在NSGA-Ⅱ算法的基础上引入外部存档储存非支配解和对父体染色体的选择方式进行改进. 在案例管网中应用NSGA-Ⅱ算法和改进的NSGA-Ⅱ算法对水质监测点多目标选址模型进行求解. 结果显示: 在获得完整的非支配最优解的情况下

使用改进的NSGA-Ⅱ算法

相比于使用NSGA-Ⅱ算法节省了约42%的运算时间

提高了求解的效率.

Abstract

To improve the solution searching efficiency of NSGA-Ⅱ in the multi-objective optimization of water quality monitoring station

an external archive is established to store non-dominant solution and the option of the paternal chromosomes improved on the basis of NSGA-Ⅱ. NSGA-Ⅱand improved NSGA-Ⅱare respectively used to solve the multi-objective optimal placement of water quality monitoring station in example network. The result shows that: comparing with NSGA-Ⅱ

the improved NSGA-Ⅱsaves about 42% of the operation time and improves the efficiency of solving in the complete non-dominant optimal solution. The improved NSGA-Ⅱis more applicable to solve multi-objective optimal placement of water quality monitoring station in actual network.

关键词

Keywords

references

LYU M, QIU Q J, LI N H, et al . Study on the security of city water supply systems[J ] . Journal of Qingdao Technological University, 2005, 26(1): 1-4.

BEHZADIAN K, KAPELAN Z, SAVIC D, et al . Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks[J ] . Environmental Modelling Software, 2009, 24(4): 530-541.

BERRY J, HART W E, PHILLIPS C A, et al . Sensor placement in municipal water networks with temporal integer programming models[J ] . Journal of Water Resources Planning Management, 2006, 132(132): 218-224.

TAO T, LYU C Z, XIN K L, et al . Optimal layout of monitoring stations for detecting accidental contaminations in water distribution system[J ] . Journal of Tongji Univesity (Natural Science Edition), 2010, 11: 1621-1625.

EHSANI N, AFSHAR A. Application of NA-ACO in multiobjective contaminant sensor network design for water distribution systems[C]//Water Distribution Systems Analysis: ASCE, 2011: 327-337.

DEB K, AGRAWAL S, PRATAP A, et al. A fast elitist Non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II[C]//Parallel Problem Solving From Nature Vi. [s.n.]: Springer, 2000: 849-858.

TAN Q R, WANG L G, ZHONG D Y. Application of NSGA-II in multi-objective route optimization of underground Mine’s Trans-portation[J]. Gold Science and Technology, 2016, 24(2): 95-100.

HUA C C, WANG Y J, LI J P, et al . Multi-objective optimization model for blast furnace production and ingredients based on NSGA-Ⅱalgorithm[J ] . CIESC Journal, 2016, 67(3): 1040 -1047.

LI C H, HE Y. The NSGA-Ⅱ application optimization allocation of water resources in Karamay[J]. Ground Water, 2016, 38(2): 126 -129.

ZHANG Z B, ZHANG S H, JIANG Y Z, et al . Application of improved genetic algorithm in reservoir optimal operation [J ] . Yellow River, 2012, 34(8): 54-56.

JIN X, GANG J L, ZHANG J, et al . Optimal rehabilitation model of water supply network with non-domina ted genetic algorithm-II [J ] . Journal of Harbin Institute of Technology University, 2008, 40(12): 1969-1976.

HE Z H, YUAN Y X. Reliability optimization design of water distribution system based on surplus energy entropy[J]. Journal of Zhejiang University (Engineering Science), 2014, 48 (7): 1188-1194.

LIU S M, LI M M, WANG H H, et al . Multi-objective optimization design of water distribution system based on non-dominated sorting genetic algorithms-Ⅱ[J ] . China Water Wastewater, 2015(5): 50-53.

QIAO J F, WEI J, HAN H G. Multi-objective optimization of water distribution system based on an improved NSGA2 algorithm [J]. Control Engineering of China, 2016, 23 (12): 1861-1866.

LIU S M, WU X, OUYANG L Y. Method for optimal sensor placement in water distribution systems with nodal demand uncertainties [J]. Environmental Science, 2013, 34(8): 3108-3112.

ZHOU S K, XU S R. An optimal locating of quality monitoring stations in urban water distribution systems [J]. Journal of Nanhua University of South China(Natural Science Edition), 2004, 18(3): 62-66.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

储能变流器电能质量治理策略研究

基于数字混频的脉冲式激光测距方法的研究与实现

基于高效时序建模的RGB-T跟踪

SCA-YOLO: 基于可变形卷积与上下文感知注意力的实时目标检测

具守恒量波动方程的循环双子网物理信息神经网络