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    This study introduces a novel multiobjective differential evolution (MODE) algorithm. It enhances population distribution and convergence using a reference axis vicinity mechanism (RAVM) and a hybrid control strategy.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Differential Evolution (DE) is effective for multiobjective optimization.
    • Existing multiobjective DE (MODE) methods often overlook population distribution in the objective space.
    • Prior research focused on parameter control and mutation operators, neglecting distribution issues.

    Purpose of the Study:

    • To propose a new MODE variant addressing population distribution problems.
    • To introduce a Reference Axis Vicinity Mechanism (RAVM) for restoring population distribution and convergence.
    • To develop a hybrid control strategy for accelerating convergence.

    Main Methods:

    • Development of a Reference Axis Vicinity Mechanism (RAVM).
    • Integration of RAVM into the multiobjective differential evolution framework.
    • Implementation of a hybrid control strategy for parameters and mutation operators.
    • Performance evaluation on benchmark multiobjective optimization problems.

    Main Results:

    • The proposed MODE with RAVM and hybrid strategy demonstrates competitive performance.
    • The algorithm shows superiority over several state-of-the-art multiobjective evolutionary algorithms.
    • RAVM effectively restores good population distribution and maintains convergence.

    Conclusions:

    • The novel MODE variant with RAVM and hybrid control strategy is effective.
    • This approach improves upon existing multiobjective evolutionary algorithms.
    • The proposed methods enhance both distribution and convergence in multiobjective optimization.