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    Area of Science:

    • Optical Physics
    • Particle Characterization
    • Computational Science

    Background:

    • Accurate particle-size distribution (PSD) is crucial in various scientific and industrial applications.
    • Retrieving PSD from light-scattering data is an inverse problem often requiring sophisticated algorithms.
    • Existing methods may face challenges with convergence and accuracy for specific distribution types.

    Purpose of the Study:

    • To develop and validate a stochastic inverse method for retrieving monomodal particle-size distribution (PSD).
    • To utilize a hybrid evolutionary optimization algorithm (HEOA) for solving the inverse light-scattering problem.
    • To assess the HEOA's performance with various unimodal distributions (normal, gamma, lognormal) and size parameters.

    Main Methods:

    • A hybrid evolutionary optimization algorithm (HEOA) was employed, combining a multimember evolution strategy and locally weighted linear regression.
    • The Fraunhofer approximation was used within the HEOA framework to solve the optimization problem.
    • Mie theory was utilized to generate light-scattering intensity patterns for numerical simulations.

    Main Results:

    • The HEOA successfully converged to near-optimal solutions for the objective function.
    • The algorithm accurately retrieved monomodal PSDs from simulated angular light-scattering distributions.
    • Satisfactory performance was demonstrated for spherical particles with modal size parameters ranging from 46 to 150.

    Conclusions:

    • The developed stochastic inverse method based on HEOA is a robust tool for PSD retrieval.
    • The HEOA offers an effective approach to solving the inverse light-scattering problem for unimodal distributions.
    • This method shows significant potential for applications requiring precise particle characterization.