Abstract: A steam humidity measurement model and a wet steam parameter inversion optimization model were established based on the laser back-axis angular scattering method in order to measure the steam trubine humidity at the final stage accurately. According to the optimization models, the particle swarm optimization (PSO)algorithm was used to perform multiple inversion optimizations on the simulation data which was added the Gaussian white noise and the experimental data of the simulated cylinder. The obtained inversion results were compared with the artificial fish-swarm algorithm and the traditional uniform search method as well. When the PSO algorithm was used, the inversion results of r_0.5, K and N were 0.05, 0.66 and 0.51% respectively, the inversion time was 306.41 s. When the artificial fish-swarm algorithm was used, the inversion results of r_0.5, K and N were 2.96, 19.98 and 4.68% respectively, the inversion time was 411.05 s. When the uniform search algorithm was used, the inversion results of r_0.5, K and N were 5.00, 27.14 and 7.95% respectively, the inversion time was 246.42 s. The results show that the PSO algorithm can overcome the shortcomings of both artificial fish-swarm algorithm and uniform search method, which can obtain high precision, stable and reliable inversion results in a short time. It provides more accurate data for wet steam parameter measurement and theoretical basis for other particle parameter measurement inversions.