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Updated: Jul 7, 2026

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Simulated-annealing-based genetic algorithm for modeling the optical constants of solids.

A B Djurisi, J M Elazar, A D Raki

    Applied Optics
    |February 12, 2008
    PubMed
    Summary

    A novel simulated-annealing-based genetic algorithm enhances model parameter estimation. This hybrid approach outperforms standard genetic algorithms in finding global minima and improving parameter accuracy for optical materials.

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    Area of Science:

    • Computational physics
    • Materials science
    • Optimization algorithms

    Background:

    • Model parameter estimation is crucial for understanding material properties.
    • Accurate estimation requires robust optimization techniques to avoid local minima.
    • Existing methods like genetic algorithms can struggle with complex, multi-modal landscapes.

    Purpose of the Study:

    • To develop and evaluate a hybrid algorithm combining simulated annealing and genetic algorithms for parameter estimation.
    • To compare the performance of the proposed algorithm against traditional genetic algorithms.
    • To assess the algorithm's efficacy in finding global minima and improving parameter accuracy.

    Main Methods:

    • A simulated-annealing-based genetic algorithm (SA-GA) was developed.
    • SA-GA was tested on synthetic data mimicking metal optical constants.
    • Performance was benchmarked against standard genetic algorithms (GA).
    • Key metrics included global minimum convergence and parameter estimation accuracy.

    Main Results:

    • The SA-GA demonstrated superior performance compared to the plain GA.
    • The hybrid algorithm showed enhanced ability to locate the global minimum.
    • More accurate model parameter values were obtained using the SA-GA.
    • The algorithm successfully fitted the dielectric function model to platinum and aluminum data.

    Conclusions:

    • The simulated-annealing-based genetic algorithm is an effective approach for complex parameter estimation problems.
    • This hybrid method offers improved accuracy and global convergence over standard genetic algorithms.
    • The algorithm shows promise for applications in materials science, particularly in optical property analysis.