JACIII Vol.28 No.3 pp. 484-493
doi: 10.20965/jaciii.2024.p0484

Research Paper:

Leakage Source Location of Hazardous Chemicals Based on the Improved Gray Wolf Optimization Algorithm

Zeng-Qiang Chen ORCID Icon, Yi-Meng Wang, Cong-Cong Qi, and Shao-Kun Zheng

Information Engineering College, Beijing Institute of Petrochemical Technology
No.19 Qingyuan North Road, Huangcun Town, Daxing District, Beijing 102600, China

Corresponding author

May 30, 2023
November 1, 2023
May 20, 2024
source inversion, Gaussian diffusion model, leakage source location, gray wolf optimization algorithm

To accurately determine the leakage source location and strength during gas leakage accidents, this study compares the concentration obtained from the diffusion model with that measured by the sensor and proposes an improved gray wolf optimization algorithm for leakage source location. This algorithm introduces two improvement strategies. First, a nonlinear convergence factor is introduced to balance the global and local searches of the algorithm. Second, a reverse learning operation is performed on the three individuals with the worst fitness in the contemporary population. The results showed that the location results based on the improved gray wolf optimization algorithm exhibited high accuracy and stability, could quickly and accurately locate the leakage source, and provided data support for emergency disposal of accidents.

IGWO-based leakage source location

IGWO-based leakage source location

Cite this article as:
Z. Chen, Y. Wang, C. Qi, and S. Zheng, “Leakage Source Location of Hazardous Chemicals Based on the Improved Gray Wolf Optimization Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 484-493, 2024.
Data files:
  1. [1] P. Mlakar, M. Z. Božnar, and B. Breznik, “Operational Air Pollution Prediction and Doses Calculation in Case of Nuclear Emergency at Krško Nuclear Power Plant,” Int. J. of Environment and Pollution, Vol.54, Nos.2-4, pp. 184-192, 2014.
  2. [2] S. K. Singh, M. Sharan, and J.-P. Issartel, “Inverse Modelling Methods for Identifying Unknown Releases in Emergency Scenarios: An Overview,” Int. J. of Environment and Pollution, Vol.57, No.1/2 pp. 68-91, 2015.
  3. [3] M. D. C. Freitas, A. P. Marques, M. A. Reis, and M. M. Farinha, “Atmospheric Dispersion of Pollutants in Sado Estuary (Portugal) Using Biomonitors,” Int. J. of Environment and Pollution, Vol.32, No.4, pp. 434-455, 2008.
  4. [4] X. Zheng and Z. Chen, “Back-Calculation of the Strength and Location of Hazardous Materials Releases Using the Pattern Search Method,” J. of Hazardous Materials, Vol.183, Issues 1-3, pp. 474-481, 2010.
  5. [5] A. Cantelli, F. D’orta, A. Cattini, F. Sebastianelli, and L. Cedola, “Application of Genetic Algorithm for the Simultaneous Identification of Atmospheric Pollution Sources,” Atmospheric Environment, Vol.115, pp. 36-46, 2015.
  6. [6] L. C. Thomson, B. Hirst, G. Gibson, S. Gillespie, P. Jordan, K. D. Skeldon, and M. J. Padgett, “An Improved Algorithm for Locating a Gas Source Using Inverse Methods,” Atmospheric Environment, Vol.41, pp. 1128-1134, 2007.
  7. [7] J. Sujitha and K. Baskaran, “Genetic Gray Wolf Optimizer Based Channel Estimation in Wireless Communication System,” Wireless Personal Communications, Vol.99, pp. 965-984, 2018.
  8. [8] T. Ma, S. Liu, and H. Xiao, “Location of Natural Gas Leakage Sources on Offshore Platform by a Multi-Robot System Using Particle Swarm Optimization Algorithm,” J. of Natural Gas Science and Engineering, Vol.84, Article No.103636, 2020.
  9. [9] F. Zitouni, S. Harous, and R. Maamri, “A Novel Quantum Firefly Algorithm for Global Optimization,” Arabian J. for Science and Engineering, Vol.46, pp. 8741-8759, 2021.
  10. [10] Y. Hou, L. Zhao, and H. Lu, “Fuzzy Neural Network Optimization and Network Traffic Forecasting Based on Improved Differential Evolution,” Future Generation Computer Systems, Vol.81, pp. 425-432, 2018.
  11. [11] M. Pant, M. Ali, and V. P. Singh, “Differential Evolution Using Quadratic Interpolation for Initializing the Population,” 2009 IEEE Int. Advance Computing Conf., pp. 375-380, 2009.
  12. [12] G. Shang, J. Xinzi, and T. Kezong, “Hybrid Algorithm Combining Ant Colony Optimization Algorithm With Genetic Algorithm,” 2007 Chinese Control Conf., pp. 701-704, 2007.
  13. [13] Y. Li, H. Wu, and Y. Sun, “Improved Adaptive Genetic Algorithm Based RFID Positioning,” J. Systems Engineering and Electronics, Vol.33, Issue 2, pp. 305-311, 2022.
  14. [14] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Gray Wolf Optimizer,” Advances in Engineering Software, Vol.69, pp. 46-61, 2014.
  15. [15] S. Dereli, “A New Modified Gray Wolf Optimization Algorithm Proposal for a Fundamental Engineering Problem in Robotics,” Neural Computing and Applications, Vol.33, pp. 14119-14131, 2021.
  16. [16] X. Jin, X. Zhang, H. Jiang, and J. Tian, “Hybrid Strategy Improved Gray Wolf Optimization Algorithm for Plume Tracking and Localization Method in Indoor Weak Wind Environment,” IEEE Access, Vol.10, pp. 100976-100986, 2022.
  17. [17] C. Zhang, W. Wang, and Y. Pan, “Enhancing Electronic Nose Performance by Feature Selection Using an Improved Gray Wolf Optimization Based Algorithm,” Sensors, Vol.20, No.15, Article No.4065, 2020.
  18. [18] W. Shen, M. Xiao, Z. Wang, and X. Song, “Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Gray Wolf Algorithm,” Sensors, Vol.23, No.14, Article No.6645, 2023.
  19. [19] Y. Hou, H. Gao, Z. Wang, and C. Du, “Improved Gray Wolf Optimization Algorithm and Application,” Sensors, Vol.22, No.10, Article No.3810, 2022.
  20. [20] H. R. Tizhoosh, “Opposition-Based Learning: A New Scheme for Machine Intelligence,” Int. Conf. on Computational Intelligence for Modelling, Control and Automation and Int. Conf. on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC’06), pp. 695-701, 2005.
  21. [21] S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Opposition-Based Differential Evolution,” IEEE Trans. on Evolutionary Computation, Vol.12, Issue 1, pp. 64-79, 2008.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Jun. 03, 2024