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A Decomposition-Based Many-Objective Artificial Bee Colony Algorithm.

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    A new decomposition-based artificial bee colony (ABC) algorithm effectively handles many-objective optimization problems (MaOPs). This hybrid approach balances convergence and diversity, offering improved performance and efficiency for complex optimization tasks.

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    Area of Science:

    • Computational intelligence
    • Optimization algorithms
    • Evolutionary computation

    Background:

    • Many-objective optimization problems (MaOPs) present significant challenges due to the difficulty in maintaining solution diversity and convergence.
    • Existing evolutionary algorithms often struggle with the curse of dimensionality in MaOPs.
    • Artificial Bee Colony (ABC) algorithms are known for their fast convergence but may lack diversity maintenance in high-dimensional objective spaces.

    Purpose of the Study:

    • To propose a novel decomposition-based artificial bee colony (ABC) algorithm tailored for many-objective optimization problems (MaOPs).
    • To enhance the balance between convergence speed and solution diversity in MaOPs.
    • To improve the efficiency and quality of solutions for approximating nondominated solutions in MaOPs.

    Main Methods:

    • A decomposition strategy is employed to divide MaOPs into smaller, manageable subproblems.
    • A modified ABC algorithm is utilized to simultaneously optimize these subproblems.
    • Dynamic resource allocation is implemented using specialized onlooker and scout bees to address subproblems unequally.

    Main Results:

    • The proposed hybrid algorithm demonstrates superior or comparable performance against five state-of-the-art many-objective evolutionary algorithms on 13 test problems with up to 50 objectives.
    • Experimental results confirm improvements in both the quality of the final solution set and algorithmic efficiency.
    • Statistical analysis (Wilcoxon signed-rank test) validates the significant contribution of specialized onlooker and scout bees to performance enhancement.

    Conclusions:

    • The decomposition-based ABC algorithm effectively addresses the challenges of MaOPs by leveraging the strengths of both decomposition and ABC.
    • The algorithm achieves a favorable balance between convergence and diversity, leading to high-quality, well-distributed nondominated solutions.
    • This proposed method presents a promising and efficient tool for tackling complex many-objective optimization problems.