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    This study introduces an adaptive multifactorial evolutionary algorithm (MFEA-AKT) that dynamically selects crossover operators for improved knowledge transfer in evolutionary multitasking. MFEA-AKT enhances convergence and solution quality across diverse optimization problems.

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    Area of Science:

    • Evolutionary Computation
    • Multitask Optimization
    • Artificial Intelligence

    Background:

    • Multifactorial Evolutionary Algorithm (MFEA) optimizes multiple tasks simultaneously, leveraging knowledge transfer for improved performance.
    • Current MFEA variants use fixed crossover operators, limiting adaptability and potentially hindering robust search performance.
    • Effective knowledge transfer via crossover is crucial for MFEA's success, but its adaptive configuration remains unexplored.

    Purpose of the Study:

    • To investigate the impact of different crossover operators on knowledge transfer within MFEA for single-objective (SO) and multiobjective (MO) problems.
    • To propose and evaluate a novel MFEA with adaptive knowledge transfer (MFEA-AKT) for enhanced multitask optimization.
    • To address the gap in adaptive crossover configuration for knowledge transfer in MFEA.

    Main Methods:

    • Empirical analysis of various crossover operators' effects on MFEA performance in SO and MO continuous optimization.
    • Development of MFEA-AKT, featuring a self-adaptive mechanism for selecting knowledge transfer crossover operators during the evolutionary process.
    • Comprehensive experimental validation on SO and MO multitask benchmark problems.

    Main Results:

    • MFEA-AKT successfully identifies appropriate knowledge transfer crossovers dynamically, adapting to different problems and search stages.
    • The adaptive approach leads to superior or competitive performance compared to MFEAs employing fixed crossover operators.
    • Demonstrated effectiveness of adaptive knowledge transfer in improving convergence speed and solution quality in multitask optimization.

    Conclusions:

    • Adaptive configuration of crossover operators significantly enhances the robustness and efficiency of MFEA for multitask optimization.
    • MFEA-AKT offers a more effective strategy for knowledge transfer, outperforming fixed-operator approaches.
    • The proposed MFEA-AKT represents a significant advancement in evolutionary multitasking optimization.