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    This study introduces an event-triggered impulsive (ETI) control scheme to enhance differential evolution (DE) algorithms. The novel approach improves both exploration and exploitation, boosting DE performance on benchmark functions.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Control Theory

    Background:

    • Differential evolution (DE) is a widely used evolutionary algorithm.
    • Existing DE algorithms can be improved for better search performance.

    Purpose of the Study:

    • To introduce an event-triggered impulsive (ETI) control scheme to enhance DE.
    • To improve the exploitation and exploration abilities of DE algorithms.

    Main Methods:

    • Implemented an event-triggered impulsive (ETI) control scheme within the DE framework.
    • Introduced two types of impulses: stabilizing and destabilizing.
    • Triggered impulsive control (IPC) when population update rate declines or becomes zero.

    Main Results:

    • The ETI scheme significantly improved the performance of DE algorithms.
    • Experimental results on IEEE CEC 2014 benchmark functions demonstrated effectiveness.
    • The proposed method enhances both exploitation and exploration capabilities.

    Conclusions:

    • The ETI control scheme is a simple yet effective method for improving DE algorithms.
    • The approach offers flexibility for integration with various DE variants.
    • This research advances the application of impulsive control in evolutionary computation.