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    Area of Science:

    • Optimization Algorithms
    • Evolutionary Computation
    • Multiobjective Optimization

    Background:

    • Pareto dominance-based evolutionary algorithms struggle with many objectives (M ≥ 4) due to poor Pareto optimality discriminability.
    • Existing non-Pareto dominance methods face challenges with irregular Pareto front shapes in many-objective problems.

    Purpose of the Study:

    • To propose a new, effective, and simple many-objective evolutionary algorithm for handling problems with numerous objectives.
    • To address the limitations of existing algorithms in maintaining both convergence and diversity in many-objective optimization.

    Main Methods:

    • Introduce a novel algorithm based on the generalization of Pareto optimality (GPO).
    • Employ an "(M-1) + 1" framework of GPO dominance, termed (M-1)-GPD, for ranking solutions during environmental selection.
    • Utilize M symmetrical cases of (M-1)-GPD to enhance selection pressure on M-1 objectives while preserving the remaining objective.

    Main Results:

    • The proposed algorithm demonstrates competitive performance against state-of-the-art methods on various scalable benchmark problems.
    • Experiments on three real-world problems show the algorithm outperforms existing methods.

    Conclusions:

    • The proposed GPO-based algorithm effectively addresses many-objective optimization problems.
    • The (M-1)-GPD dominance framework successfully promotes simultaneous convergence and diversity, offering a robust solution for complex optimization tasks.