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    This study introduces CMME, a novel evolutionary algorithm for constrained many-objective optimization problems (CMaOPs). CMME effectively balances feasibility, convergence, and diversity, outperforming existing methods.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Limited research exists for constrained many-objective optimization problems (CMaOPs) compared to many-objective optimization problems (MaOPs).
    • Existing algorithms struggle to simultaneously balance feasibility, convergence, and diversity, which is crucial for CMaOPs.

    Purpose of the Study:

    • To propose a novel constrained many-objective optimization evolutionary algorithm (CMME) designed to effectively handle CMaOPs.
    • To enhance the balance of feasibility, convergence, and diversity in solving CMaOPs.

    Main Methods:

    • Developed CMME featuring two novel ranking strategies for mating and environmental selections.
    • Incorporated a new individual density estimation method combined with crowding distance to improve diversity.
    • Utilized θ-dominance to enhance selection pressure for convergence and diversity.

    Main Results:

    • CMME demonstrated superior performance on 13 benchmark CMaOPs and 3 real-world applications.
    • Experimental results show CMME's effectiveness in balancing feasibility, convergence, and diversity.
    • The proposed algorithm outperformed nine related existing algorithms.

    Conclusions:

    • CMME offers a robust solution for constrained many-objective optimization problems.
    • The integrated selection strategies and diversity promotion mechanisms contribute to CMME's effectiveness.
    • CMME presents a competitive and superior alternative for CMaOP research and application.