Compared with conventional non-flowing particles, an increase in flow velocity of aerosol particles can exacerbate the ill posedness of the inversion equation and increase sensitivity to noise, which makes it difficult to obtain accurate regularization parameters. To improve the accuracy of selecting regularization parameters for flowing aerosol particles, an improved grey wolf algorithm based on weighted Morozov discrepancy (WMD-IGWO) is proposed to optimize the regularization parameters on the basis of the traditional Morozov discrepancy principle. This method obtains the noise component of the electric field ACF through wavelet packet decomposition, and establishes an objective function by weighting the deviation function based on the noise component, which can reduce the impact of noise on the data. The convergence factor of GWO is nonlinearly improved, and the objective function is incorporated into the IGWO for global optimization to obtain the optimal regularization parameter, thereby enhancing the accuracy of the inversion results. The inversion results of four simulated aerosol particles (292, 483, 167/575, 208/733 nm) at different flow velocity show that compared with the L-curve method, the WMD-IGWO inversion results in smaller distribution errors and peak position errors of particle size distribution (PSD), and higher accuracy of inversion results. The inversion results of 584 nm unimodal and 243/825 nm bimodal measured particles show that WMD-IGWO can reduce peak position errors by up to 0.041 and 0.116/0.087, respectively, which is superior to the inversion results of the L-curve method and verifies the conclusions of the simulation experiment.