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    A new clustering-based adaptive multiobjective evolutionary algorithm (CA-MOEA) effectively solves complex optimization problems with irregular Pareto fronts, improving diversity and convergence for better results.

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    Area of Science:

    • Computational Intelligence
    • Optimization Algorithms
    • Evolutionary Computation

    Background:

    • Existing multiobjective evolutionary algorithms (MOEAs) excel with continuous Pareto fronts but struggle with discontinuous or degenerated fronts.
    • Irregular Pareto fronts in multiobjective optimization problems (MOPs) present significant challenges for current algorithmic approaches.

    Purpose of the Study:

    • To introduce a novel clustering-based adaptive MOEA (CA-MOEA) designed to address MOPs characterized by irregular Pareto fronts.
    • To enhance diversity and accelerate convergence in multiobjective optimization through adaptive cluster center generation.

    Main Methods:

    • Development of the CA-MOEA, incorporating adaptive cluster center generation for guiding selection.
    • Rigorous performance evaluation of CA-MOEA across 18 standard benchmark MOPs.
    • Application of CA-MOEA to optimize parameters in the carbon fiber formation process.

    Main Results:

    • CA-MOEA demonstrates competitive performance in multiobjective optimization, particularly on MOPs with irregular Pareto fronts.
    • The algorithm shows significant improvements in maintaining diversity and accelerating convergence compared to existing methods.
    • Successful application of CA-MOEA in optimizing stretching parameters for carbon fiber production.

    Conclusions:

    • CA-MOEA offers a robust solution for multiobjective optimization problems with challenging, irregular Pareto fronts.
    • The adaptive clustering approach is key to CA-MOEA's effectiveness in diverse and complex optimization landscapes.