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    This study introduces MOMAD, a novel memetic algorithm for combinatorial multiobjective optimization. MOMAD effectively decomposes complex problems, outperforming existing methods on benchmark instances.

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    Area of Science:

    • Computational Intelligence
    • Operations Research
    • Computer Science

    Background:

    • Combinatorial multiobjective optimization problems (CMOPs) are prevalent in various fields.
    • Existing algorithms often struggle with the complexity and scale of CMOPs.
    • There is a need for efficient and adaptable algorithms for CMOPs.

    Purpose of the Study:

    • To propose a simple yet efficient memetic algorithm, MOMAD, for CMOPs.
    • To provide a generic hybrid framework for integrating problem-specific knowledge and search strategies.
    • To evaluate MOMAD's performance against state-of-the-art algorithms.

    Main Methods:

    • MOMAD decomposes CMOPs into single-objective subproblems using an aggregation method.
    • It employs three populations: P(L) for current solutions, P(P) for Pareto local search initialization, and P(E) for nondominated solutions.
    • Combines Pareto local search with single-objective local search iteratively.

    Main Results:

    • Extensive experiments were conducted on the multiobjective traveling salesman and knapsack problems.
    • MOMAD demonstrated competitive or superior performance compared to existing state-of-the-art algorithms.
    • The algorithm's effectiveness in handling complex CMOPs was validated.

    Conclusions:

    • MOMAD offers a flexible and powerful framework for CMOPs.
    • The proposed algorithm is an anytime algorithm, providing solutions at any stage of the search.
    • MOMAD represents a significant advancement in solving combinatorial multiobjective optimization problems.