Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (18): 21-33.DOI: 10.3778/j.issn.1002-8331.1906-0252

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Survey of Application of Swarm Intelligence Algorithm in Gas Source Location

WANG Wei, CUI Yihao, WANG Tong, ZHU Tianyu, TIAN Liqin   

  1. 1.School of Information & Electrical Engineering, Hebei University of Engineering, Handan, Hebei 056038, China
    2.Hebei Key Laboratory of Security & Protection Information Sensing and Processing, Handan, Hebei 056038, China
    3.School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
    4.Hebei Engineering Technology Research Center for IoT Data Acquisition & Processing, North China Institute of Science and Technology, Langfang, Hebei 065201, China
  • Online:2019-09-15 Published:2019-09-11



  1. 1.河北工程大学 信息与电气工程学院,河北 邯郸 056038
    2.河北省安防信息感知与处理重点实验室,河北 邯郸 056038
    3.江南大学 物联网工程学院,江苏 无锡 214122
    4.华北科技学院 河北省物联网数据采集与处理工程技术研究中心,河北 廊坊 065201

Abstract: In recent years, frequent gas leakage incidents make gas source location becoming an urgent problem in the field of public safety. Gas source location problem can be transformed into optimization problem in essence. As an efficient optimization algorithm, swarm intelligence algorithm provides a new solution for it. The research background and status quo of gas source location are introduced. The representative research results are classified, summarized and compared according to the research ideas and contents of swarm intelligence algorithm applied in gas source location. The existing problems and future development trend of gas source location research based on swarm intelligence algorithm are analyzed and discussed. The prospect will provide some reference for further research on gas source location.

Key words: gas source localization, swarm intelligence algorithm, ant colony algorithm, particle swarm algorithm

摘要: 近几年频繁发生的气体泄漏事件使得气体源定位成为了公共安全领域亟待解决的问题。气体源定位问题本质上可以转化为最优化问题,群智能算法作为一种高效的优化算法,为其提供了一个全新的解决方案。介绍了气体源定位问题的研究背景和研究现状;根据群智能算法在气体源定位中应用的研究思路和研究内容对具有代表性研究成果进行了分类综述和对比分析;对目前基于群智能算法的气体源定位研究中存在的问题和未来发展趋势进行了分析和展望,对气体源定位问题的进一步研究提供一定的参考作用。

关键词: 气体源定位, 群智能算法, 蚁群算法, 粒子群算法