Two Improved Multiobjective Fractional-Order Particle Swarm Optimization Algorithms for True Temperature Inversion of Multiwavelength Pyrometer

In this article, two improved fractional-order particle swarm optimization (IFOPSO) algorithms for the true temperature inversion of high-temperature targets with unknown emissivity are presented by transforming multispectral true temperature inversion into multiobjective minimum optimization. Combi...

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Vydáno v:IEEE sensors journal Ročník 24; číslo 13; s. 21191 - 21199
Hlavní autoři: Liang, Mei, Sun, Zhuo, Wang, Changhui
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1530-437X, 1558-1748
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Shrnutí:In this article, two improved fractional-order particle swarm optimization (IFOPSO) algorithms for the true temperature inversion of high-temperature targets with unknown emissivity are presented by transforming multispectral true temperature inversion into multiobjective minimum optimization. Combining the smoothing of exponential inertia weights and the advantages of adaptive adjustment according to the actual situation, as well as the advantages of fractional-order jumping out of local extreme values, the exponentially decreasing inertia weight (Ediw) IFOPSO algorithm is proposed. Meanwhile, the linear varying inertia weights (Lviws) IFOPSO, which combines time-varying acceleration coefficient and linear inertia weight, is designed to improve the multiobjective optimization ability and the accuracy of real temperature inversion. The typical emissivity model and the measured data of rocket tail flame are used for simulation, and the effectiveness of the proposed method is verified.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2024.3403095