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    This study introduces a novel minimax differential evolution algorithm for robust design problems. The new approach improves efficiency and handles diverse problem types, outperforming existing methods.

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    Area of Science:

    • Optimization Algorithms
    • Robust Design
    • Computational Intelligence

    Background:

    • Minimax optimization is crucial for robust design, aiming for optimal worst-case performance.
    • Existing minimax algorithms face limitations in problem scope, computational cost, and efficiency.
    • There is a need for advanced algorithms to overcome these challenges in robust design.

    Purpose of the Study:

    • To propose a novel minimax differential evolution algorithm.
    • To address the limitations of existing minimax optimization approaches.
    • To enhance efficiency and applicability in robust design problems.

    Main Methods:

    • Developed a minimax differential evolution algorithm incorporating a bottom-boosting scheme for identifying promising solutions.
    • Implemented a partial-regeneration strategy and a new mutation operator for comprehensive solution space exploration.
    • Integrated these mechanisms to create a versatile algorithmic structure.

    Main Results:

    • The proposed algorithm demonstrated statistical superiority compared to seven established methods in empirical evaluations.
    • The algorithm successfully addressed two open problems in robust design, validating its effectiveness.
    • The novel mechanisms contribute to reliable and efficient identification and exploration of solutions.

    Conclusions:

    • The proposed minimax differential evolution algorithm offers a superior approach to robust design problems.
    • The algorithm overcomes limitations of existing methods regarding problem type, computational cost, and efficiency.
    • This work provides an effective and versatile tool for tackling complex minimax optimization challenges.