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    This study enhances the cooperative coevolutionary algorithm for the capacitated arc routing problem (CARP). The improved method addresses limitations in solution generation and resource allocation, achieving better results for single and multi-objective large scale CARP.

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    Area of Science:

    • Operations Research
    • Computer Science
    • Artificial Intelligence

    Background:

    • The capacitated arc routing problem (CARP) is crucial for social applications.
    • Existing algorithms like RDG-MAENS have limitations in solution quality and resource allocation.
    • Previous methods were restricted to single-objective CARP.

    Purpose of the Study:

    • To overcome drawbacks in the RDG-MAENS algorithm for CARP.
    • To develop an improved algorithm for both single and multi-objective large scale CARP (LSCARP).
    • To enhance solution generation and resource allocation within cooperative coevolutionary algorithms.

    Main Methods:

    • Improved RDG-MAENS by immediate solution updating and shared area application.
    • A novel allocation scheme based on route direction vector magnitude.
    • Integration with a decomposition-based memetic algorithm for multi-objective LSCARP.

    Main Results:

    • The enhanced algorithm demonstrates superior performance on single-objective LSCARP.
    • Significant improvements were observed for multi-objective LSCARP.
    • The proposed allocation scheme balances sub-population resource distribution effectively.

    Conclusions:

    • The improved RDG-MAENS algorithm effectively addresses limitations of prior methods.
    • The enhanced approach provides better solutions for both single and multi-objective LSCARP.
    • This work contributes a more efficient and balanced algorithm for large scale routing problems.