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    A new method, parallel Pareto local search based on decomposition (PPLS/D), enhances multiobjective optimization. This approach uses parallel processing and problem decomposition to significantly outperform existing Pareto local search variants.

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    Area of Science:

    • Operations Research
    • Computer Science
    • Artificial Intelligence

    Background:

    • Pareto local search (PLS) is fundamental to metaheuristics for multiobjective combinatorial optimization.
    • Existing PLS variants face challenges in efficiency and scalability for complex problems.

    Purpose of the Study:

    • To introduce an enhanced Pareto local search variant, parallel PLS based on decomposition (PPLS/D).
    • To improve the efficiency of multiobjective combinatorial optimization using parallel computation and problem decomposition.

    Main Methods:

    • PPLS/D decomposes the search space into multiple subregions for parallel processing.
    • Each parallel process is guided by a unique scalar objective function for fine-grained search control.
    • The method was experimentally evaluated on multiobjective unconstrained binary quadratic programming and traveling salesman problems.

    Main Results:

    • PPLS/D demonstrated significantly superior performance compared to basic PLS and another PLS variant.
    • The enhanced performance was consistent across different initial solution generation methods (random and heuristic).
    • The method proved effective even with up to four objectives.

    Conclusions:

    • PPLS/D offers a substantial improvement in efficiency and effectiveness for multiobjective combinatorial optimization.
    • The proposed approach represents a promising advancement in metaheuristic design for complex optimization tasks.