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    Area of Science:

    • Computational Intelligence
    • Operations Research
    • Computer Science

    Background:

    • Decomposition-based algorithms transform multiobjective optimization problems (MOPs) into subproblems.
    • Existing decomposition methods may struggle with population diversity and collaborative search.
    • Single-objective local search (LS) is effective for combinatorial MOPs within a decomposition framework.

    Purpose of the Study:

    • To propose a dynamic constrained decomposition with grids (DCDG) framework.
    • To design a multiobjective memetic algorithm (DCDG-MOMA) using the DCDG framework.
    • To enhance population diversity and collaborative search in solving MOPs.

    Main Methods:

    • Developed the dynamic constrained decomposition with grids (DCDG) framework.
    • Integrated DCDG into a multiobjective memetic algorithm (DCDG-MOMA).
    • Employed dynamic grid adjustments to increase nondominated solutions and improve collaborative search.

    Main Results:

    • DCDG-MOMA demonstrated effectiveness in maintaining population diversity.
    • The framework facilitated better collaborative local search among subproblems.
    • The algorithm achieved competitive or superior performance compared to existing methods.

    Conclusions:

    • The DCDG framework offers an effective approach for decomposition-based multiobjective optimization.
    • DCDG-MOMA provides a robust method for solving combinatorial MOPs.
    • Dynamic grid adjustments are beneficial for enhancing solution quality and diversity.