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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Engineering Applications

    Background:

    • Large-scale multiobjective optimization problems (LSMOPs) involve optimizing numerous conflicting objectives simultaneously.
    • Real-world engineering applications often demand algorithms with consistent performance across multiple runs (insensitivity).
    • Existing LSMOP algorithms primarily focus on performance, neglecting crucial insensitivity characteristics.

    Purpose of the Study:

    • To develop an evolutionary algorithm that improves both performance and insensitivity for LSMOPs.
    • To address the limitations of current algorithms in handling the performance sensitivity inherent in large-scale problems.
    • To enhance the reliability of optimization solutions for practical engineering scenarios.

    Main Methods:

    • Proposing a novel evolutionary algorithm integrating Monte Carlo tree search (MCTS) for LSMOPs.
    • Utilizing MCTS to sample decision variables, construct nodes, and guide optimization and evaluation.
    • Implementing a node selection strategy based on good evaluations to mitigate performance sensitivity.
    • Developing and applying two novel metrics to quantify algorithm sensitivity.

    Main Results:

    • The proposed algorithm demonstrates effectiveness in solving LSMOPs.
    • Experimental results confirm the algorithm's superior performance compared to state-of-the-art methods.
    • The proposed approach significantly improves the insensitivity (robustness) of the optimization process.
    • Validation conducted on diverse benchmark functions and using proposed sensitivity metrics.

    Conclusions:

    • The developed large-scale multiobjective optimization algorithm via Monte Carlo tree search effectively addresses performance and insensitivity.
    • The integration of MCTS provides a robust framework for tackling complex engineering optimization challenges.
    • The findings suggest a promising direction for creating more reliable optimization tools for large-scale, real-world problems.