|本期目录/Table of Contents|

[1]冯崧,曾祥进,黄瑜豪.基于改进烟花优化算法的三维空间气体源定位*[J].中国安全生产科学技术,2024,20(3):69-76.[doi:10.11731/j.issn.1673-193x.2024.03.010]
 FENG Song,ZENG Xiangjin,HUANG Yuhao.Localization of gas source in three-dimensional space based on improved fireworks optimization algorithm[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2024,20(3):69-76.[doi:10.11731/j.issn.1673-193x.2024.03.010]
点击复制

基于改进烟花优化算法的三维空间气体源定位*
分享到:

《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
20
期数:
2024年3期
页码:
69-76
栏目:
职业安全卫生管理与技术
出版日期:
2024-03-31

文章信息/Info

Title:
Localization of gas source in three-dimensional space based on improved fireworks optimization algorithm
文章编号:
1673-193X(2024)-03-0069-08
作者:
冯崧曾祥进黄瑜豪
(1.武汉工程大学 计算机科学与工程学院,湖北 武汉 430205;
2.湖北三峡实验室,湖北 宜昌 445804;
3.武汉工程大学 荆门化工新材料产业技术研究院,湖北 荆门 448000)
Author(s):
FENG Song ZENG Xiangjin HUANG Yuhao
(1.School of Computer Science and Engineering,Wuhan Institute of Technology,Wuhan Hubei 430205,China;
2.Hubei Three Gorges Laboratory,Yichang Hubei 445804,China;
3.Jingmen Institute of Chemical Industry and New Materials Industry Technology,Wuhan Institute of Technology,Jingmen Hubei 448000,China)
关键词:
气体源定位烟花爆炸算法莱维飞行灰狼优化算法
Keywords:
gas source localization fireworks algorithm Lévy flight grey wolf optimization algorithm
分类号:
X937;X169
DOI:
10.11731/j.issn.1673-193x.2024.03.010
文献标志码:
A
摘要:
为探究三维空间中气体源定位及其源强反算问题,提出1种改进烟花爆炸算法(GWOFA)。将定位过程分为全局定位过程和局部定位过程。全局定位过程即结合灰狼优化算法和莱维飞行在三维空间中的全局搜索过程;局部定位过程是在全局定位的结果上的进一步开发过程,其通过引入边界条件的爆炸半径选取方式和选择策略更加高效地改进烟花优化算法实现。研究结果表明:本文算法相比于支持向量机回归模型(LinearSVR)、GWO算法和粒子群算法具有更高精确度,相比于GWO算法和粒子群算法具有更高稳定性和更低随机性;在气体源定位问题上,本文算法整体表现优于LinearSVR、GWO算法和粒子群算法。研究结果可为解决三维空间中气体源定位问题和相关参数估计问题提供新的思路方法。
Abstract:
In order to investigate the problems of gas source localization and back-calculation of source intensity in the three-dimensional space,an improved fireworks explosion algorithm (GWOFA) was proposed.The localization process is divided into two stages: the global localization and the local localization.The global localization stage combines the grey wolf optimization (GWO) algorithm with the Lévy flight for global exploration in three-dimensional space.The local localization process refers to the further development process based on the results of global localization,which is achieved through the introduction of boundary conditions for selecting the explosion radius and a more efficient selection strategy through an improved fireworks optimization algorithm.The results show that the algorithm proposed in this paper has higher accuracy compared to support vector machine regression model (LinearSVR),GWO algorithm,and particle swarm algorithm,as well as higher stability and lower randomness compared to GWO algorithm and particle swarm algorithm.In terms of gas source localization,the overall performance of this algorithm is superior to LinearSVR,GWO algorithm,and particle swarm algorithm.The results can provide new ideas and methods for solving the problems of gas source localization and related parameters estimation in the three-dimensional spaces.

参考文献/References:

[1]何娟.基于神经网络和演化算法的气体源定位研究[D].成都:电子科技大学,2022.
[2]ALLEN C T,YOUNG G S,HAUPT S E.Improving pollutant source characterization by better estimating wind direction with a genetic algorithm[J].Atmospheric Environment,2007,41(11):2283-2289.
[3]CAO M L,MENG Q H,ZENG M,et al.Distributed least-squares estimation of a remote chemical source via convex combination in wireless sensor networks[J].Sensors,2014,14(7):11444-11466.
[4]MA D L,TAN W,WANG Q S,et al.Application and improvement of swarm intelligence optimization algorithm in gas emission source identification in atmosphere[J].Journal of Loss Prevention in the Process Industries,2018,56:262-271.
[5]MA D L,TAN W,WANG Q S,et al.Location of contaminant emission source in atmosphere based on optimal correlated matching of concentration distribution[J].Process Safety and Environment Protection,2018,57(6):4238-4254.
[6]MA D L,GAO J M,ZHANG Z X,et al.An improved firefly algorithm for gas emission source parameter estimation in atmosphere[J].IEEE Access,2019,7:111923-111930.
[7]LIU Y,JIANG Y,ZHANG X,et al.Combined grey wolf optimizer algorithm and corrected Gaussian diffusion model in source term estimation[J].Processes,2022,10(7):1238-1253.
[8]BARAD M L.Project prairie grass,a field program in diffusion[J].Geophysical Research Paper,1958,59:1-305.
[9]陈增强,高艺博,陈成功,等.基于差分进化-NM单纯形法的危化品泄漏源定位[J].中国安全生产科学技术,2022,18(5):90-95.CHEN Zenqiang,GAO Yibao,CHEN Chenggong,et al.Location for leakage source of hazardous chemicals based on differential evolution-NM simplex method[J].Journal of Safety Science and Technology,2022,18(5):90-95.
[10]黄浪尘,许诺,张诚.基于改进GA-PSO算法的三维WSN气体源定位研究[J].实验室研究与探索,2022,41(12):138-143.HUANG Langchen,XU Nuo,ZHANG Cheng.Research on 3D WSN gas source location based on improved GA-PSO algorithm[J].Research and Exploration in Laboratory,2022,41(12):138-143.
[11]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69:46-61.
[12]TAN Y,ZHU Y C.Fireworks algorithm for optimization[J].Advances in Swarm Intelligence,2010,6145:355-364.
[13]YANG X S,DEB S.Engineering optimisation by cuckoo search[J].International Journal of Mathematical Modelling and Numerical Optimisation,2010,1(4):330-343.
[14]ROHIT S,URVINDER S,SUPREET S,et al.Self-adaptive salp swarm algorithm for engineering optimization problems[J].Applied Mathematical Modelling,2021,89(1):188-207.
[15]MANTEGNA R N.Fast,accurate algorithm for numerical simulation of Lévy stable stochastic processes[J].Physical Review E,1994,49(5):4677-4683.
[16]唐家福,穆平安,周天媛.基于布谷鸟算法的数字散斑相关方法优化[J].陕西理工大学学报(自然科学版),2019,35(4):62-65,72.TANG Jiafu,MU Ping’an,ZHOU Tianyuan.Optimization of digital speckle correlation method based on cuckoo algorithm[J].Journal of Shaanxi University of Technology(Natural Science Edition),2019,35(4):62-65,72.

相似文献/References:

备注/Memo

备注/Memo:
收稿日期: 2023-09-19
* 基金项目: 湖北省湖北三峡实验室创新基金项目(SC215001);武汉工程大学荆门化工新材料产业技术研究院开放基金项目(JM2023006)
作者简介: 冯崧,硕士研究生,主要研究方向为气体泄漏事故态势感知。
通信作者: 曾祥进,博士,副教授,主要研究方向为智能机器人控制、机器视觉、嵌入式系统设计。
更新日期/Last Update: 2024-04-07