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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Optimization

    Background:

    • Evolutionary multi-objective optimization (EMO) algorithms are effective for multi-criteria decision-making.
    • Comparing EMO algorithms is challenging due to their black-box nature, hindering analysis of internal evolutionary processes.
    • Visual analytics tools have shown promise in explainable AI, suggesting potential for EMO algorithm comparison.

    Purpose of the Study:

    • To develop an interactive visual analytics framework for comparing the evolutionary processes of multiple EMO algorithms.
    • To enhance the comparative analysis of EMO algorithms by providing insights into their internal workings.
    • To support analysts in exploring and understanding diverse algorithms and their solution sets.

    Main Methods:

    • Literature review and expert interviews to identify analytical tasks.
    • Development of a multi-faceted visualization design.
    • Framework application to benchmarking and real-world multi-objective optimization problems.

    Main Results:

    • The proposed framework enables interactive exploration and comparison of EMO algorithm evolutionary processes.
    • Visualizations support the analysis of both intermediate generations and final solution sets.
    • Case studies demonstrate the framework's effectiveness in inspecting and comparing diverse algorithms.

    Conclusions:

    • Interactive visualization significantly enhances the comparative analysis of EMO algorithms.
    • The visual analytics framework provides valuable tools for understanding and differentiating algorithm behaviors.
    • This approach facilitates deeper insights into multi-objective optimization processes.