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    This study introduces a novel decomposition-based coevolutionary algorithm for many-objective optimization problems. The algorithm enhances parallel population evolution and uses new aggregation functions for improved diversity and faster convergence.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Many-objective optimization problems (MaOPs) present significant challenges due to the difficulty in approximating the Pareto optimal front.
    • Existing evolutionary algorithms often struggle with scalability and maintaining diversity in high-dimensional objective spaces.

    Purpose of the Study:

    • To develop a novel decomposition-based coevolutionary algorithm (DECA) for effectively solving many-objective optimization problems.
    • To enhance the exploration and exploitation capabilities of evolutionary algorithms for MaOPs.
    • To improve population diversity and accelerate convergence towards the Pareto optimal front.

    Main Methods:

    • The proposed algorithm decomposes MaOPs into subproblems using well-distributed weight vectors, assigning each subpopulation to a specific subproblem.
    • A mating pool strategy collects elite individuals from cooperative subpopulations to breed offspring, enhancing exploration.
    • Novel aggregation functions are designed for environmental selection to promote diversity and convergence speed.

    Main Results:

    • The DECA algorithm was rigorously tested against state-of-the-art many-objective evolutionary algorithms on numerous benchmark instances.
    • Performance was also evaluated on a real-world engineering design problem.
    • Experimental results demonstrate the competitiveness of the proposed algorithm.

    Conclusions:

    • The decomposition-based coevolutionary approach offers a promising strategy for tackling many-objective optimization problems.
    • The novel aggregation functions contribute to enhanced population diversity and faster convergence.
    • The proposed algorithm shows strong performance on both benchmark and real-world problems, indicating its practical applicability.