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 is presented by transforming multi-spectral true temperature inversion into multi-objective 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 (Lviw) IFOPSO, which combines time-varying acceleration coefficient and linear inertia weight, is designed to improve the multi-objective 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.