为了精准测量汽轮机末级蒸汽湿度，提出在激光后向异轴角散射法的基础上建立蒸汽湿度测量模型和湿蒸汽参数反演优化模型。根据该优化模型采用粒子群算法对加入高斯白噪声的仿真数据和模拟汽缸的实验数据进行反演寻优，并将得到的反演结果与人工鱼群算法和传统的均匀搜索方法进行了对比。采用粒子群算法时r0.5、K、N的反演结果误差为0.05、0.66和0.51%，反演时间为306.41 s；采用鱼群算法时r0.5、K、N的反演结果误差为2.96、19.98和4.68%，反演时间为411.05 s；采用均匀搜索算法时r0.5、K、N的反演结果误差为5.00、27.14和7.95%，反演时间为246.42 s。结果表明：粒子群算法能够克服人工鱼群算法和均匀搜索方法两者的不足，可以在较短时间内获得精度高且稳定可靠的反演结果，为湿蒸汽参数测量提供了更加准确的数据，并对其他颗粒物参数测量反演提供了理论依据。

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 r0.5,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 r0.5,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 r0.5,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.