The traditional differential evolution algorithm frequently encounters the problem of premature convergence and low accuracy. By introducing adaptive control variable factors, a differential evolution (DE) algorithm was developed to ensure that the population could be updated continuously and successfully. The individual learning, which is conducive to the evolution of subsequent populations, and the overall inversion yield more accurate results. As a validation, the particle systems obeying three typical distribution functions of Gaussian distribution, R-R distribution and lognormal distribution were numerically simulated. The resultant distribution parameter values $\bar R $ and K yield errors less than 5%, and the deviations of median volume diameter are within ±5% compared with the given distribution. The DE algorithm also reveals obvious stability and noise resistance.