Application of High-dimensional Multi-objective Differential Evolution Algorithm Based on Global Ordering in Resistance Identification of Heating Pipe Network

doi:10.19596/j.cnki.1001-246x.8825

Abstract

Abstract:

A high-dimensional multi-objective differential evolution algorithm based on global ranking is developed to identify the resistance coefficient of heat supply network, and the multi-objective algorithm is applied to the resistance identification of heat supply network, and the calculation process of resistance identification is improved. The fuzzy mathematics method is applied to the process of resistance identification, a set of optimal solution is generated by identifying each pipe segment, and the optimal solution is selected from the optimal solution set according to the fuzzy membership degree. The results show that compared with the single objective algorithm, the optimal solution set generated by the high-dimensional multi-objective differential evolution algorithm based on global ranking is uniformly distributed and concentrated, and the optimal solution obtained is more accurate.

Key words: district heating network, resistance identification, high-dimensional multi-objective optimization, differential evolution algorithm

Junli YU, Enze ZHOU, Zhuangkuo LIU, Wenxiao XU, Yingshuai YANG, Mingyu XIANG. Application of High-dimensional Multi-objective Differential Evolution Algorithm Based on Global Ordering in Resistance Identification of Heating Pipe Network[J]. Chinese Journal of Computational Physics, 2025, 42(1): 118-126.

Figures/Tables 9

References 34

1	王昭俊, 董立华, 姜永成, 等. 热计量变流量供热系统室外管网动态水力失调与控制[J]. 哈尔滨工业大学学报, 2010, 42 (2): 218-222, 258.
2	林仁祺. 枝状供热管网的非相似水力工况构造与管道阻力系数辨识研究[D]. 哈尔滨: 哈尔滨工业大学, 2021.
3	WANG Na , YOU Shijun , WANG Yaran , et al. Hydraulic resistance identification and optimal pressure control of district heating network[J]. Energy and Buildings, 2018, 170, 83- 94. DOI
4	BEKIBAYEV T , ZHAPBASBAYEV U , RAMAZANOVA G , et al. Oil pipeline hydraulic resistance coefficient identification[J]. Cogent Engineering, 2021, 8 (1): 1950303. DOI
5	KALTENBACHER S , STEINBERGER M , HORN M . Pipe roughness identification of water distribution networks: The full turbulent case[J]. Applied Mathematical Modelling, 2020, 80, 879- 894. DOI
6	ZECCHIN A C , LAMBERT M F , SIMPSON A R , et al. Parameter identification in pipeline networks: Transient-based expectation-maximization approach for systems containing unknown boundary conditions[J]. Journal of Hydraulic Engineering, 2014, 140 (6): 04014020. DOI
7	DINI M , TABESH M . A new method for simultaneous calibration of demand pattern and Hazen-Williams coefficients in water distribution systems[J]. Water Resources Management, 2014, 28 (7): 2021- 2034. DOI
8	SAVIC D A, WALTERS G A. genetic algorithm techniques for calibrating network models: 95/12[R]. Exeter: University of Exeter, 1995.
9	LINGIREDDY S, ORMSBEE L E. Optimal network calibration model based on genetic algorithms[C]//29th Annual Water Resources Planning and Management Conference. Tempe: American Society of Civil Engineers, 1999: 1-8.
10	FAN Qingwu , ZHOU Xingqi , HAN Huazheng , et al. Resistance coefficient identification of a heating pipe network based on a heuristic three-parent genetic algorithm[J]. Engineering Optimization, 2023, 55 (6): 930- 945. DOI
11	梁洪波. 供热管网阻力系数辨识与泄漏诊断[D]. 北京: 华北电力大学, 2018.
12	刘永鑫, 邹平华, 马月璇. 基于遗传算法的供水管网阻力系数辨识[J]. 中国给水排水, 2014, 30 (23): 113- 116.
13	屠丽娟, 周恩泽, 吴雪飞, 等. 基于流量测点的热网变动阻力系数优化辨识[J]. 计算物理, 2021, 38 (4): 498- 504.
14	SHERRI F , MAHVI A H , TOLOIE ESHLAGHY A , et al. A new approach in simultaneous calibration of Hazen-Williams coefficients and demand of nodes in of water distribution systems[J]. Desalination and Water Treatment, 2017, 74, 137- 148. DOI
15	吕昆. 基于混合遗传算法的管网阻抗辨识优化[J]. 锅炉制造, 2012 (3): 61- 62.
16	周志刚, 邹平华, 谈和平, 等. 基于遗传蚂蚁混合算法的热网阻力特性辨识[J]. 哈尔滨工业大学学报, 2008, 40 (11): 1761- 1765. DOI
17	姜飞. 混合智能优化算法及其应用[D]. 西安: 西安电子科技大学, 2011.
18	GAO Ying, SHI Lei, YAO Pingjing. Study on multi-objective genetic algorithm[C]//Proceedings of the 3rd World Congress on Intelligent Control and Automation (Cat. No. 00EX393), 1. Hefei: IEEE, 2000: 646-650.
19	AUGUSTO O B , BENNIS F , CARO S . A new method for decision making in multi-objective optimization problems[J]. Pesquisa Operacional, 2012, 32 (2): 331- 369. DOI
20	KONAK A , COIT D W , SMITH A E . Multi-objective optimization using genetic algorithms: A tutorial[J]. Reliability Engineering & System Safety, 2006, 91 (9): 992- 1007.
21	GRECO M , GIUDICE G D . New approach to water distribution network calibration[J]. Journal of Hydraulic Engineering, 1999, 125 (8): 849- 854. DOI
22	王海, 王海鹰, 周伟国, 等. 供热管网中管段阻力系数的辨识方法[J]. 计算物理, 2013, 30 (3): 422- 432. DOI
23	许刚, 张土乔, 吕谋, 等. 多工况的遗传算法校正管道摩阻系数[J]. 中国给水排水, 2004, 20 (8): 50- 53.
24	刘奇奇. 基于人工蜂群算法的多目标问题优化[D]. 深圳: 深圳大学, 2017.
25	谢承旺, 潘嘉敏, 郭华, 等. 一种采用混合策略的大规模多目标进化算法[J]. 计算机学报, 2024, 47 (1): 69- 89.
26	付祥钊, 肖益民. 流体输配管网[M]. 4版北京: 中国建筑工业出版社, 2018.
27	肖婧. 差分进化算法及其高维多目标优化应用[M]. 北京: 人民邮电出版社, 2018: 199.
28	肖婧, 毕晓君, 王科俊. 基于全局排序的高维多目标优化研究[J]. 软件学报, 2015, 26 (7): 1574- 1583.
29	丁义, 杨建. 欧式距离与标准化欧式距离在k近邻算法中的比较[J]. 软件, 2020, 41 (10): 135-136, 140.
30	MONTAÑO A A, COELLO C A C, MEZURA-MONTES E. MODE-LD+SS: A novel Differential Evolution algorithm incorporating local dominance and scalar selection mechanisms for multi-objective optimization[C]//IEEE Congress on Evolutionary Computation. Barcelona, Spain: IEEE, 2010: 1-8.
31	STORN R , PRICE K . Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization, 1997, 11 (4): 341- 359.
32	瞿悦呈, 陈家星, 崔国民. 加权差分进化算法应用于换热网络综合[J]. 计算物理, 2021, 38 (1): 79- 88.
33	石建平, 李培生, 刘国平. 基于混合群智能优化算法的混沌系统参数估计[J]. 计算物理, 2019, 36 (5): 621- 630.
34	吴雪飞. 基于差分进化算法的供热管网阻抗辨识及变压差优化控制方法研究[D]. 青岛: 青岛理工大学, 2022.

指标	算法	工况1	工况2	工况3	工况4
GD	DE	8.289 87×10^-4	7.684 82×10^-4	8.297 59×10^-4	7.124 2×10^-4
GD	MODE	1.521 89×10^-4	1.067 51×10^-4	1.238 06×10^-4	9.827 49×10^-5
SP	DE	1.001×10^-2	1.06×10^-2	9.94×10^-3	9.13×10^-3
SP	MODE	1.92×10^-3	1.8×10^-3	2×10^-3	1.6×10^-3

指标	算法	工况1	工况2	工况3	工况4
GD	DE	8.289 87×10^-4	7.684 82×10^-4	8.297 59×10^-4	7.124 2×10^-4
GD	MODE	1.521 89×10^-4	1.067 51×10^-4	1.238 06×10^-4	9.827 49×10^-5
SP	DE	1.001×10^-2	1.06×10^-2	9.94×10^-3	9.13×10^-3
SP	MODE	1.92×10^-3	1.8×10^-3	2×10^-3	1.6×10^-3

编号	阻力系数/Pa/(t·h^-1)²	编号	阻力系数/Pa/(t·h^-1)²	编号	阻力系数/Pa/(t·h^-1)²	编号	阻力系数/Pa/(t·h^-1)²
E1	0.024	E11	0.283	E21	1.254	U4	529.917
E2	40.507	E12	27.828	E22	8.967	U5	1 068.557
E3	0.106	E13	0.365	E23	33.167	U6	821.867
E4	17.510	E14	1.548	E24	15.687	U7	246.768
E5	0.085	E15	9.599	E25	7.414	U8	295.601
E6	34.618	E16	28.307	E26	18.176	U9	396.599
E7	0.129	E17	16.527	E27	39.254	U10	412.076
E8	24.720	E18	17.863	U1	438.436	U11	872.400
E9	45.968	E19	0.213	U2	501.091
E10	15.257	E20	35.093	U3	864.900

编号	阻力系数/Pa/(t·h^-1)²	编号	阻力系数/Pa/(t·h^-1)²	编号	阻力系数/Pa/(t·h^-1)²	编号	阻力系数/Pa/(t·h^-1)²
E1	0.024	E11	0.283	E21	1.254	U4	529.917
E2	40.507	E12	27.828	E22	8.967	U5	1 068.557
E3	0.106	E13	0.365	E23	33.167	U6	821.867
E4	17.510	E14	1.548	E24	15.687	U7	246.768
E5	0.085	E15	9.599	E25	7.414	U8	295.601
E6	34.618	E16	28.307	E26	18.176	U9	396.599
E7	0.129	E17	16.527	E27	39.254	U10	412.076
E8	24.720	E18	17.863	U1	438.436	U11	872.400
E9	45.968	E19	0.213	U2	501.091
E10	15.257	E20	35.093	U3	864.900

[1]	NI Xiaowei, XU Sihui, FENG Jiaming, LIU Diren. Fast Inversion of Array Laterolog Based on Adaptive Differential Evolution Algorithm [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2019, 36(4): 465-473.
[2]	DUAN Huanhuan, CUI Guomin, CHEN Jiaxing, CHEN Shang. A Strategy of Differential Evolution with Opposition-based Multi-population Parallel [J]. CHINESE JOURNAL OF COMPUTATIONAL PHYSICS, 2016, 33(5): 561-569.