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Development and simulation of two novel indoor odor source localization methods using a modified shark smell optimization algorithm
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DOI：10.1016/j.measurement.2024.115562.png)
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Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell optimization (SSO) algorithm and modified it for OSL tasks. Subsequently, mechanisms for collective information sharing and preventing falling into local minima were incorporated, leading to the development of the Improved Shark Smell Optimization (I-SSO) algorithm. We tested both algorithms in a computational fluid dynamics (CFD) simulated environment with a single interference source and compared them to the particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). In scenarios with one and two interference sources. The results showed that the I-SSO algorithm outperformed the other three algorithms in both environment settings, demonstrating a higher success rate and superior search distance efficiency.
Keyword:
Odor source localization
Particle swarm optimization algorithm
Shark smell optimization algorithm
Whale optimization algorithm
Swarm intelligence
期刊
IF:
5.6
论文数:
2.0W
被引数:
5.4W
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