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    A new cooperative framework optimizes fuzzy systems using multiobjective evolutionary algorithms. This approach enhances fuzzy controller performance for mobile robot navigation, achieving effective wall-following control.

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    Area of Science:

    • Artificial Intelligence
    • Computational Intelligence
    • Robotics

    Background:

    • Fuzzy systems (FSs) require optimization of numerous parameters.
    • Existing methods may struggle with complex multiobjective optimization tasks.

    Purpose of the Study:

    • To introduce a novel cooperative framework for multiobjective evolutionary fuzzy systems.
    • To develop a multiobjective algorithm for optimizing fuzzy system parameters.

    Main Methods:

    • A multiobjective rule-based cooperative continuous ant-colony optimization (MO-RCCACO) algorithm was developed.
    • The MO-RCCACO utilizes multiple subcolonies and an auxiliary colony for enhanced optimization.
    • Pareto nondominated sorting and crowding distance were employed for rule performance ranking.

    Main Results:

    • The MO-RCCACO algorithm successfully optimized fuzzy controllers (FCs) for mobile robot wall following.
    • Comparative analyses demonstrated the superior optimization ability of MO-RCCACO.
    • Experimental validation confirmed the effectiveness of MO-RCCACO-based FCs in real-world robot control.

    Conclusions:

    • The proposed cooperative framework and MO-RCCACO algorithm offer an effective approach for multiobjective fuzzy system optimization.
    • MO-RCCACO-based fuzzy controllers are highly effective for mobile robot boundary following tasks.