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Efficient solution to the stagnation problem of the particle swarm optimization algorithm for phase diversity.

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    Area of Science:

    • Optics
    • Computational Science

    Background:

    • Phase diversity (PD) technique requires optimization algorithms for accurate wavefront sensing.
    • Particle swarm optimization (PSO) is a suitable algorithm for PD due to its efficiency and global search capabilities.
    • Traditional PSO suffers from premature convergence, leading to suboptimal solutions in PD.

    Purpose of the Study:

    • To address the stagnation problem in PSO for PD.
    • To enhance the robustness and performance of PD wavefront sensing.

    Main Methods:

    • Illustrated the stagnation problem of traditional PSO for PD.
    • Proposed a novel strategy involving a premature convergence detection criterion and a redistributing mechanism.
    • Incorporated randomized Halton sequences to ensure uniform particle distribution and randomness.

    Main Results:

    • The proposed strategy effectively solves the stagnation problem of PSO for PD.
    • Demonstrated improved performance in large-scale, high-dimension wavefront sensing and noisy conditions.
    • Validated through simulations and experimental verification.

    Conclusions:

    • The developed strategy significantly enhances the robustness and performance of PD wavefront sensing.
    • Offers a reliable solution for overcoming PSO limitations in optical wavefront analysis.