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    This study introduces a novel distributed differential evolution algorithm (DDE-AMS) that adaptively manages subpopulations for efficient large-scale optimization. DDE-AMS demonstrates competitive performance against state-of-the-art methods.

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    Area of Science:

    • Computational intelligence
    • Optimization algorithms

    Background:

    • Large-scale optimization problems are prevalent across numerous research domains.
    • Efficiently solving these problems requires advanced algorithmic approaches.

    Purpose of the Study:

    • To propose a distributed differential evolution algorithm with adaptive mergence and split (DDE-AMS) for large-scale optimization.
    • To enhance resource allocation for subpopulations based on their performance.

    Main Methods:

    • Developed novel adaptive mergence and split operators for subpopulations.
    • Implemented a parallel master-slave approach for distributed computation.
    • Conducted extensive experiments on 20 large-scale benchmark functions.

    Main Results:

    • DDE-AMS achieved competitive or superior performance compared to state-of-the-art algorithms.
    • Demonstrated effective adaptive resource allocation and scalability.
    • Analyzed the impact of DDE-AMS components, adaptive behavior, and parameter sensitivity.

    Conclusions:

    • DDE-AMS effectively addresses large-scale optimization challenges through adaptive subpopulation management.
    • The algorithm shows promising scalability and efficiency in parallel computing environments.