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A Many-Objective Evolutionary Algorithm Based on a Two-Round Selection Strategy.

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    This study introduces a two-round environmental selection strategy to improve evolutionary algorithms for many-objective optimization problems. The novel approach effectively balances population diversity and convergence, enhancing algorithm performance.

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    Area of Science:

    • Computational Intelligence
    • Optimization Theory
    • Algorithm Design

    Background:

    • Balancing population diversity and convergence is crucial for evolutionary algorithms tackling many-objective optimization problems (MaOPs).
    • Existing algorithms often struggle to achieve a good trade-off between these two critical aspects.
    • Many-objective evolutionary algorithms (MaOEAs) require advanced selection mechanisms.

    Purpose of the Study:

    • To propose a novel two-round environmental selection strategy for MaOEAs.
    • To enhance the trade-off between population diversity and convergence in evolutionary computation.
    • To introduce a new algorithm, 2REA, incorporating this strategy.

    Main Methods:

    • A two-round environmental selection process is developed.
    • Neighborhood density is calculated using an adaptive position transformation strategy.
    • A candidate pool is formed by selecting solutions with low neighborhood density.
    • The best convergent solution from the pool is inserted into the next generation.

    Main Results:

    • The proposed two-round selection strategy is integrated into an MaOEA framework, creating the 2REA algorithm.
    • 2REA demonstrated competitive performance against eight state-of-the-art MaOEAs on benchmark problems.
    • Experimental results confirm the strategy's effectiveness in balancing diversity and convergence.

    Conclusions:

    • The proposed two-round environmental selection strategy successfully balances population diversity and convergence for MaOEAs.
    • The 2REA algorithm shows strong performance and competitiveness in many-objective optimization.
    • This strategy offers a promising direction for improving MaOEA design.