Localization of Hazardous Chemical Leakage Based on Multi-Strategy Improved Harris Hawks Optimization Algorithm

doi:10.16183/j.cnki.jsjtu.2024.452

Abstract

Abstract:

Hazardous chemical gas leakage accidents pose significant threats to public safety and the environment, and how to accurately locate the leakage source strength and location is particularly important. Aiming at the defects of traditional leak source inversion methods such as long duration and low accuracy, this paper proposes an efficient inversion method based on a multi-strategy improved Harris hawks optimization (HHO) algorithm. First, the position of the Harris hawks swarm is initialized using Logistic-Tent composite chaotic mapping to improve population diversity and global search capability. Then, an elite level strategy is employed to scientifically divide the population, thereby enhancing the local search ability and optimization accuracy of the algorithm. Finally, a logarithmic function is adopted to flexibly adjust the escape energy and achieve a smooth transition in algorithm performance. The results show that the improved HHO algorithm has achieved significant improvements in both optimization efficiency and inversion accuracy. Even under complex terrain conditions, it can accurately control the relative error of the leakage source intensity and location within a maximum of 1.00%. This method greatly improves the accuracy and response speed of leakage source localization, providing a reliable solution for emergency decision-making in accidents.

Key words: Harris hawks optimization (HHO) algorithm, Logistic-Tent composite chaotic mapping, source inversion, nonlinear decay

CLC Number:

TP301.6

CHEN Zengqiang, QI Congcong, ZHAO Yiwen, CHENG Yi. Localization of Hazardous Chemical Leakage Based on Multi-Strategy Improved Harris Hawks Optimization Algorithm[J]. Journal of Shanghai Jiao Tong University, 2026, 60(5): 860-870.

Figures/Tables 10

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References 21

[1]	崔益豪, 王巍, 王彤, 等. 三维空间的自适应气体源定位方法[J]. 计算机工程与设计, 2020, 41(11): 3241-3248.
	CUI Yihao, WANG Wei, WANG Tong, et al. Adaptive gas source localization method in three-dimensional space[J]. Computer Engineering and Design, 2020, 41(11): 3241-3248.
[2]	LU J, HUANG M, WU W, et al. Application and improvement of the particle swarm optimization algorithm in source-term estimations for hazardous release[J]. Atmosphere, 2023, 14(7): 1168. doi: 10.3390/atmos14071168 URL
[3]	高春艳, 刘冬乐, 李满宏, 等. 基于仿生行为的气味源定位方法综述[J]. 科学技术与工程, 2022, 22(36): 15860-15869.
	GAO Chunyan, LIU Dongle, LI Manhong, et al. A review of odor source localization methods based on bionic behavior[J]. Science Technology and Engineering, 2022, 22(36): 15860-15869.
[4]	CHO J, KIM H, GEBRESELASSIE A L, et al. Deep neural network and random forest classifier for source tracking of chemical leaks using fence monitoring data[J]. Journal of Loss Prevention in the Process Industries, 2018, 56: 548-558. doi: 10.1016/j.jlp.2018.01.011 URL
[5]	李鹏, 单钰强. 基于协方差拟合的离格气体泄漏源定位方法[J]. 电子器件, 2023, 46(2): 478-484.
	LI Peng, SHAN Yuqiang. Off-grid gas leak source localization method based on covariance fitting[J]. Electronic Devices, 2023, 46(2): 478-484.
[6]	WANG L, YAO Y, LUO X, et al. A critical review on intelligent optimization algorithms and surrogate models for conventional and unconventional reservoir production optimization[J]. Fuel, 2023, 350: 128826. doi: 10.1016/j.fuel.2023.128826 URL
[7]	陈国华, 胡雪薇, 周利兴, 等. 基于鸡群算法的危险化学品泄漏扩散事故反演方法[J]. 华南理工大学学报(自然科学版), 2021, 49(9): 56-64. doi: 10.12141/j.issn.1000-565X.200695
	CHEN Guohua, HU Xuewei, ZHOU Lixing, et al. Inversion method for hazardous chemical leakage and diffusion accidents based on chicken swarm algorithm[J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(9): 56-64.
[8]	董文轩, 陈建国, 黄宇, 等. 基于无人小车嗅觉的混合型气体溯源定位算法[J]. 无人系统技术, 2019, 2(5): 30-38.
	DONG Wenxuan, CHEN Jianguo, HUANG Yu, et al. Hybrid gas source tracing and localization algorithm based on unmanned vehicle olfaction[J]. Unmanned Systems Technology, 2019, 2(5): 30-38.
[9]	蒋彦, 滕国伟, 徐正蓺, 等. 基于混合差分进化-模式搜索算法的泄漏源源强及位置反算方法[J]. 工业控制计算机, 2019, 32(12): 66-68.
	JIANG Yan, TENG Guowei, XU Zhengyi, et al. Inversion method for leak source strength and location based on hybrid differential evolution-pattern search algorithm[J]. Industrial Control Computer, 2019, 32(12): 66-68.
[10]	陈增强, 高艺博, 陈成功, 等. 基于改进型萤火虫群优化算法泄漏源源强及位置反算研究[J]. 中国安全生产科学技术, 2022, 18(10): 150-155.
	CHEN Zengqiang, GAO Yibo, CHEN Chenggong, et al. Inversion study on leak source strength and location based on improved firefly swarm optimization algorithm[J]. Journal of Safety Science and Technology, 2022, 18(10): 150-155.
[11]	王吉, 张儒, 李俊明. 基于改进型遗传算法的危险气体多泄漏源定位研究[J]. 武汉理工大学学报(信息与管理工程版), 2020, 42(6): 486-493.
	WANG Ji, ZHANG Ru, LI Junming. Research on multi-leak source localization of hazardous gas based on improved genetic algorithm[J]. Journal of Wuhan University of Technology (Information & Management Engineering), 2020, 42(6): 486-493.
[12]	HEIDARI A A, MIRIALILI S, FARIS H, et al. Harris hawks optimization: Algorithm and applications[J]. Future Generation Computer Systems, 2019, 97: 849-872. doi: 10.1016/j.future.2019.02.028
[13]	GUO W, XU P, DAI F, et al. Harris hawks optimization algorithm based on elite fractional mutation for data clustering[J]. Applied Intelligence, 2022, 52(10): 11407-11433. doi: 10.1007/s10489-021-02985-0
[14]	ZOU L, ZHOU S, LI X. An efficient improved greedy Harris hawks optimizer and its application to feature selection[J]. Entropy, 2022, 24(8): 1065. doi: 10.3390/e24081065 URL
[15]	ZHANG Y J, YAN Y X, ZHAO J, et al. CSCAHHO: Chaotic hybridization algorithm of the sine cosine with Harris hawk optimization algorithms for solving global optimization problems[J]. PloS One, 2022, 17(5): e0263387. doi: 10.1371/journal.pone.0263387 URL
[16]	黄鹤, 高永博, 茹锋, 等. 基于自适应黏菌算法优化的无人机三维路径规划[J]. 上海交通大学学报, 2023, 57(10): 1282-1291. doi: 10.16183/j.cnki.jsjtu.2022.191
	HUANG He, GAO Yongbo, RU Feng, et al. Three-dimensional path planning for unmanned aerial vehicles based on optimization using an adaptive slime mold algorithm[J]. Journal of Shanghai Jiao Tong University, 2023, 57(10): 1282-1291.
[17]	李锐, 龙文高, 原可欣. 基于精英策略改进的自适应布谷鸟搜索算法[J]. 智能计算机与应用, 2021, 11(12): 37-42.
	LI Rui, LONG Wengao, YUAN Kexin. Elite strategy improved adaptive cuckoo search algorithm[J]. Intelligent Computers and Applications, 2021, 11(12): 37-42.
[18]	秦秋霞, 梁仲月, 徐毅. 基于Logistic-Tent混沌映射和位平面的图像加密算法[J]. 大连民族大学学报, 2022, 24(3): 245-252.
	QIN Qiuxia, LIANG Zhongyue, XU Yi. Image encryption algorithm based on logistic-tent chaotic mapping and bit-plane[J]. Journal of Dalian Minzu University, 2022, 24(3): 245-252.
[19]	倪静, 秦斌, 曾凡龙. 一种混合策略改进的灰狼优化算法[J]. 软件导刊, 2021, 20(5): 72-76.
	NI Jing, QIN Bin, ZENG Fanlong. A hybrid strategy improved grey wolf optimization algorithm[J]. Software Guide, 2021, 20(5): 72-76.
[20]	李建山. 基于高斯烟羽模型的巷道内天然气井泄漏扩散研究[J]. 中国石油和化工标准与质量, 2020, 40(8): 185-187.
	LI Jianshan. Study on leakage and diffusion of natural gas wells in tunnels based on gaussian plume model[J]. China Petroleum and Chemical Standards and Quality, 2020, 40(8): 185-187.
[21]	常波峰, 刘欣, 杨臻, 等. 基于煤矿巷道瓦斯分布模型定位瓦斯泄漏源技术研究[J]. 煤炭科技, 2023, 44(4): 119-123.
	CHANG Bofeng, LIU Xin, YANG Zhen, et al. Study on gas leak source localization technology based on gas distribution model in coal mine tunnels[J]. Coal Science and Technology, 2023, 44(4): 119-123.

参数	期望值	地形类型	HHO算法			IHHO算法
参数	期望值	地形类型	平均值	相对误差/%	标准差	平均值	相对误差/%	标准差
Q₀/(g·s^-1)	2 000	平坦地形	2 431.30	21.57	438.67	2 000.03	0.002	0.06
		复杂地形	2 450.12	22.51	450.11	2 005.01	0.25	0.15
x₀/m	50	平坦地形	47.79	4.42	0.51	49.98	0.04	0.05
		复杂地形	46.85	6.30	0.62	50.03	0.06	0.07
y₀/m	15	平坦地形	17.94	19.58	0.11	15.01	0.04	0.02
		复杂地形	18.65	24.33	0.15	15.04	0.27	0.04
z₀/m	10	平坦地形	8.76	12.38	0.09	10.06	0.62	0.01
		复杂地形	8.52	14.80	0.12	10.10	1.00	0.02

算法	参数	期望值	平均值	相对误差/%	标准差
遗传算法	Q₀/(g·s^-1)	2 000	2 312.01	15.62	122.54
	x₀/m	50	44.98	10.04	1.76
	y₀/m	15	18.34	22.27	1.23
	z₀/m	10	9.03	9.07	0.84
粒子群算法	Q₀/(g·s^-1)	2 000	2 176.32	8.82	95.23
	x₀/m	50	47.62	4.76	0.93
	y₀/m	15	16.28	8.53	0.67
	z₀/m	10	10.54	5.40	0.45
马尔可夫链算法	Q₀/(g·s^-1)	2 000	2 123.45	6.17	105.56
	x₀/m	50	48.11	3.78	1.02
	y₀/m	15	16.72	11.47	0.89
	z₀/m	10	10.37	3.70	0.59
IHHO算法	Q₀/(g·s^-1)	2 000	2 005.01	0.25	0.15
	x₀/m	50	50.03	0.06	0.07
	y₀/m	15	15.04	0.27	0.04
	z₀/m	10	10.10	1.00	0.02