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    Area of Science:

    • Multiobjective Optimization
    • Evolutionary Algorithms
    • Decision Making

    Background:

    • Traditional methods often generate numerous solutions, overwhelming decision makers (DMs).
    • Identifying a specific region of interest (ROI) is crucial for efficient decision-making.
    • Existing methods may struggle to find preferred solutions in complex, high-objective problems.

    Purpose of the Study:

    • To develop a systematic method for incorporating DM preferences into decomposition-based evolutionary multiobjective optimization.
    • To steer the search process towards a user-defined ROI.
    • To handle problems with many objectives effectively.

    Main Methods:

    • A nonuniform mapping scheme is proposed to adjust reference points based on DM aspirations.
    • The method incorporates DM preference information into decomposition-based evolutionary algorithms.
    • The approach allows for direct or interactive steering of the search towards the ROI.

    Main Results:

    • The proposed method successfully guides the search towards the ROI.
    • It effectively handles problems with a large number of objectives (2 to 10).
    • Solutions on the boundary of the ROI can be approximated based on DM requirements.

    Conclusions:

    • The developed method provides a systematic way to approximate preferred solutions within a defined ROI.
    • The ROI's extent is intuitively understandable and controllable.
    • The approach demonstrates effectiveness across various benchmark problems with multiple objectives.