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Improved Gaussian Puff Model and Grey Wolf Optimization Algorithm for Gas Source Localization
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DOI：10.3390/app152412948.png)
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Abstract
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For gas source localization in three-dimensional space, the leakage diffusion of the gas source is first modeled as a superposition of multiple Gaussian puffs. An improved Fireworks Optimization Algorithm is then proposed, which incorporates elements from the Grey Wolf Optimization algorithm and the Fireworks Algorithm. The localization process is divided into two stages: global localization and local localization. The global localization stage integrates the GWO algorithm with L & eacute;vy flight to facilitate global exploration in three-dimensional space. The local localization stage enhances the search operators and selection strategies of the Fireworks Algorithm to perform local exploitation based on the results of the global localization. To investigate the applicability of the Grey Wolf Optimization algorithm and the Fireworks Algorithm, their performance is tested in noisy environments and with different sensor arrays. The experimental results indicate that the algorithms perform well under Gaussian noise with a variance of 0.4 and with more than nine valid sensors. Furthermore, a comparison is made between the Grey Wolf Optimization algorithm, the Fireworks Algorithm, and Particle Swarm Optimization. The simulation experiments demonstrate that the proposed algorithm exhibits greater stability, enhanced computational efficiency, and reduced randomness compared to both the Grey Wolf Optimization algorithm and Particle Swarm Optimization. It requires fewer valid sensor measurements and achieves higher accuracy in scenarios with limited valid sensor information. Overall, in the context of gas source localization, it outperforms both the Grey Wolf Optimization algorithm and Particle Swarm Optimization.
Keywords:
improved gaussian puff model
fireworks algorithm
grey wolf optimization
gas localization
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