Leakage Source Location of Hazardous Chemicals Based on the Improved 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 algorith...

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Bibliographic Details
Published inJournal of advanced computational intelligence and intelligent informatics Vol. 28; no. 3; pp. 484 - 493
Main Authors Chen, Zeng-Qiang, Wang, Yi-Meng, Qi, Cong-Cong, Zheng, Shao-Kun
Format Journal Article
LanguageEnglish
Published Tokyo Fuji Technology Press Co. Ltd 01.05.2024
Subjects
Accidents
Accuracy
Algorithms
Animal populations
Genetic algorithms
Kinematics
Leakage
Localization
Machine learning
Optimization
Optimization algorithms
Wolves
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Summary: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.
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ISSN:1343-0130
1883-8014
DOI:10.20965/jaciii.2024.p0484