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Self-Adaptive Differential Evolution Algorithm With Zoning Evolution of Control Parameters and Adaptive Mutation

Qinqin Fan, Xuefeng Yan

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    This study introduces a self-adaptive differential evolution (DE) algorithm that autonomously adjusts mutation strategies and control parameters. This adaptive approach enhances search capabilities and improves overall performance compared to existing DE variants.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Differential evolution (DE) algorithm performance is sensitive to mutation strategies and control parameters.
    • Sustaining search capability across diverse parameter combinations during evolution is challenging.

    Purpose of the Study:

    • To propose a novel self-adaptive DE algorithm.
    • To enhance DE performance through adaptive mutation strategies and zoned parameter evolution.

    Main Methods:

    • Developed a self-adaptive DE algorithm with automatic adjustment of mutation strategies.
    • Implemented zoning evolution for control parameters to enable autonomous self-adaptation.
    • Compared the proposed algorithm against five state-of-the-art DE variants using benchmark test functions.
    • Utilized seven nonparametric statistical tests for rigorous analysis of experimental results.

    Main Results:

    • The proposed self-adaptive DE algorithm demonstrated superior performance.
    • Experimental results showed significant improvements over five existing DE algorithm variants.
    • Statistical tests confirmed the enhanced effectiveness of the adaptive approach.

    Conclusions:

    • The proposed self-adaptive DE algorithm effectively enhances search capability.
    • Autonomous adaptation of mutation strategies and control parameters leads to improved optimization performance.
    • This approach offers a robust solution for complex optimization problems.