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    This study introduces information feedback models to enhance metaheuristic algorithms by reusing past data. These improved algorithms significantly boost solution quality in optimization tasks.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Computer Science

    Background:

    • Metaheuristic algorithms often neglect valuable data from prior iterations.
    • This oversight limits the potential for improving solution quality in later stages of optimization.

    Purpose of the Study:

    • To develop and evaluate a method for reusing historical individual information in metaheuristic algorithms.
    • To enhance the performance of metaheuristic algorithms by incorporating past data into the updating process.

    Main Methods:

    • Proposed six novel information feedback models to incorporate data from previous iterations.
    • Integrated these models into ten existing metaheuristic algorithms, creating new variants.
    • Employed a fitness weighting method to combine current and historical individual information.

    Main Results:

    • Experimental validation showed significant performance improvements of the variants over basic algorithms.
    • The enhanced algorithms demonstrated superior results on 14 standard test functions.
    • Effectiveness was further confirmed on 10 CEC 2011 real-world problems.

    Conclusions:

    • The proposed information feedback models demonstrably enhance metaheuristic algorithm performance.
    • Reusing historical data is a valuable strategy for improving optimization quality.
    • The developed variants offer superior solutions compared to traditional metaheuristic approaches.