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    Area of Science:

    • Computational Science
    • Optimization Theory
    • Evolutionary Computation

    Background:

    • Large-scale constrained multiobjective optimization problems (LSCMOPs) are prevalent in scientific and technological domains.
    • These problems present significant algorithmic challenges due to conflicting objectives, multiple constraints, and vast search spaces.

    Purpose of the Study:

    • To propose a novel dynamic subspace search-based evolutionary algorithm for effectively solving LSCMOPs.
    • To enhance convergence speed and exploration capabilities in large-scale optimization.

    Main Methods:

    • A decision variable analysis method to calculate the contribution of each variable.
    • A probability-based offspring generation strategy to prioritize low-dimensional subspaces with high-contribution variables.
    • A dynamic subspace expansion mechanism to ensure full-space exploration during evolution.

    Main Results:

    • The proposed algorithm demonstrated efficiency in solving LSCMOPs across various test problems.
    • Performance was validated on problems with 100-1000 decision variables.
    • Successful evaluation on four test suites and three real-world instances.

    Conclusions:

    • The dynamic subspace search approach effectively addresses the challenges of LSCMOPs.
    • The algorithm shows promise for accelerating convergence and improving exploration in large-scale optimization.
    • The method offers an efficient solution for complex optimization tasks in science and engineering.