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    Archiving and reusing promising difference vectors from past generations significantly enhances Differential Evolution (DE) performance. This strategy improves DE algorithms, even those winning IEEE competitions, and aids antenna array design.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Differential Evolution (DE) is an effective population-based algorithm for continuous optimization.
    • DE's mutation strategy uses scaled differences of individuals, but these vectors are not typically retained.
    • Conventional genetic algorithms (GAs) and evolution strategies (ESs) differ in their mutation approaches.

    Purpose of the Study:

    • To introduce a novel strategy for improving Differential Evolution (DE) performance.
    • To demonstrate the benefits of archiving and reusing promising difference vectors in DE.
    • To validate the proposed strategy on benchmark problems and a practical engineering application.

    Main Methods:

    • Developed a method to archive and reuse effective difference vectors from previous generations.
    • Integrated this archiving strategy with existing DE variants, including competition winners.
    • Evaluated the enhanced DE on IEEE CEC real parameter optimization benchmarks and circular antenna array design.

    Main Results:

    • The proposed strategy led to statistically significant performance improvements in most test cases.
    • The method showed effectiveness when combined with top-performing DE algorithms from IEEE CEC competitions.
    • Successful application demonstrated on the practical problem of designing circular antenna arrays.

    Conclusions:

    • Reusing archived difference vectors is a simple yet powerful enhancement for Differential Evolution.
    • The strategy offers significant performance gains with minimal computational overhead.
    • This approach has broad applicability across various DE variants and complex optimization tasks.