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    Area of Science:

    • Computational intelligence
    • Optimization algorithms

    Background:

    • Constrained multiobjective optimization problems (CMOPs) require balancing objective optimization and constraint satisfaction.
    • Existing evolutionary algorithms struggle to balance these aspects, especially with complex feasible regions.

    Purpose of the Study:

    • To propose a novel two-stage evolutionary algorithm for CMOPs.
    • To adaptively balance objective optimization and constraint satisfaction during the evolutionary process.

    Main Methods:

    • A two-stage evolutionary algorithm with adaptive fitness evaluation strategies.
    • Switching between stages based on population status to navigate infeasible and feasible regions.

    Main Results:

    • The proposed algorithm demonstrates superior performance compared to state-of-the-art methods.
    • Effectiveness shown on benchmark suites and real-world applications, particularly those with complex feasible regions.

    Conclusions:

    • The adaptive two-stage approach effectively balances objective optimization and constraint satisfaction in CMOPs.
    • The algorithm offers a robust solution for problems with challenging feasible spaces.