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    Area of Science:

    • Computational mathematics
    • Graph theory
    • Optimization algorithms

    Background:

    • The clique partitioning problem (CPP) involves partitioning graph vertices into subsets to maximize intra-subset edge weights.
    • CPP is computationally challenging (NP-hard) despite applications in data mining, engineering, and bioinformatics.

    Purpose of the Study:

    • To develop an effective evolutionary algorithm for solving the NP-hard clique partitioning problem.
    • To improve upon existing methods for finding high-quality solutions to CPP.

    Main Methods:

    • Proposed a novel evolutionary algorithm integrating a merge-divide crossover operator.
    • Incorporated a simulated annealing-based local search for refining solutions.
    • Evaluated performance on 94 benchmark instances, including classical and large-scale datasets.

    Main Results:

    • The proposed evolutionary algorithm demonstrated remarkable performance.
    • Achieved superior results compared to state-of-the-art methods on benchmark instances.
    • Analysis identified key algorithmic components contributing to performance.

    Conclusions:

    • The developed evolutionary algorithm offers an effective solution for the clique partitioning problem.
    • The algorithm and its source code can aid researchers and practitioners in CPP-related fields.