Weber distribution has better optimization accuracy and global search ability in nonlinear optimization problems. For this reason, a cuckoo search (WCS) algorithm based on Weber distribution is proposed to solve the problem of particle size distribution inversion. The WCS algorithm is used to invert the particle size distribution of unimodal and bimodal particle systems which follow Johnson’s S_B distribution, Rosin-Rammler distribution, and normal distribution, and the results are compared with those of other traditional algorithms. The results show that the overall performance of the WCS algorithm is better than that of the artificial fish swarm algorithm and the artificial bee colony algorithm, and the standard deviation of the improved four heavy-tailed distribution CS algorithm is 2-3 orders of magnitude higher than the original CS algorithm. Compared with the other three heavy-tailed distributions, the relative root mean square error of the WCS algorithm can be reduced by at least 1/2 when the scattering light energy of the objective function is added into the noise. The small angle forward scattering measurement system is used to study the unimodal particle system and bimodal mixed particle system. It is found that the relative root mean square error of the WCS algorithm is about 40% lower than that of the original CS algorithm.