In particle sizing by light extinction method, the regularization parameter plays an important role in applying regularization to find the solutionto ill-posed inverse problems. We combine the generalized cross-validation (GCV) and L-curve criteria with the Twomey-NNLS algorithmin parameter optimization. Numerical simulation and experimental validation show that the resistance of the newly developed algorithms tomeasurement errors can be improved leading to stable inversion results for unimodal particle size distribution. In particle sizing by light extinction method, the regularization parameter plays an important role in applying regularization to find the solution to ill-posed inverse problems. We combine the generalized cross-validation (GCV) and L-curve criteria with the Twomey-NNLS algorithmin parameter optimization. Numerical simulation and experimental validation show that the resistance of the newly developed algorithms tomeasurement errors can be improved leading to stable inversion results for unimodal particle size distribution.