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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Vydané v:IEEE sensors journal Ročník 24; číslo 13; s. 21191 - 21199
Hlavní autori: Liang, Mei, Sun, Zhuo, Wang, Changhui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Emissivity
Extreme values
Fractional-order particle swarm
High temperature
Inertia
Linear programming
Multiple objective analysis
multispectral radiation temperature measurement
Optimization
Particle measurements
Particle swarm optimization
pyrometer
Temperature distribution
Temperature measurement
true temperature inversion
Wavelength measurement
ISSN:1530-437X, 1558-1748
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Abstract 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.
AbstractList 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.
Author Wang, Changhui
Sun, Zhuo
Liang, Mei
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crossref_primary_10_1016_j_infrared_2025_105959
crossref_primary_10_1016_j_measurement_2024_116431
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SubjectTerms Algorithms
Emissivity
Extreme values
Fractional-order particle swarm
High temperature
Inertia
Linear programming
Multiple objective analysis
multispectral radiation temperature measurement
Optimization
Particle measurements
Particle swarm optimization
pyrometer
Temperature distribution
Temperature measurement
true temperature inversion
Wavelength measurement
Title Two Improved Multiobjective Fractional-Order Particle Swarm Optimization Algorithms for True Temperature Inversion of Multiwavelength Pyrometer
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