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Block-Level Knowledge Transfer for Evolutionary Multitask Optimization.

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    This study introduces a novel block-level knowledge transfer (BLKT) framework for evolutionary multitask optimization. BLKT enhances knowledge sharing between similar dimensions across tasks, outperforming existing methods.

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    Area of Science:

    • Evolutionary Computation
    • Multi-task Optimization
    • Artificial Intelligence

    Background:

    • Evolutionary multitask optimization (EMO) aims to solve multiple problems simultaneously.
    • Effective knowledge transfer between tasks is crucial but limited in current EMO algorithms.
    • Existing methods fail to transfer knowledge between similar/related dimensions and ignore intra-task dimension relationships.

    Purpose of the Study:

    • To propose a novel Block-Level Knowledge Transfer (BLKT) framework to address limitations in EMO knowledge transfer.
    • To enable more rational knowledge sharing between similar dimensions, regardless of task or alignment.
    • To improve the performance of evolutionary algorithms in solving multi-task optimization problems.

    Main Methods:

    • The proposed BLKT framework divides individuals into blocks representing consecutive dimensions.
    • Similar blocks from the same or different tasks are clustered for collaborative evolution.
    • BLKT was integrated with differential evolution (DE) to create the BLKT-DE algorithm.

    Main Results:

    • Extensive experiments on CEC17, CEC22 benchmarks, a new composite test suite, and real-world problems demonstrated BLKT-DE's superior performance.
    • BLKT-DE significantly outperformed state-of-the-art EMO algorithms.
    • BLKT-DE also showed promise in single-task optimization, achieving competitive results.

    Conclusions:

    • The BLKT framework offers a more effective and rational approach to knowledge transfer in EMO.
    • BLKT-DE represents a significant advancement in solving multi-task optimization problems.
    • The BLKT approach has potential applications beyond EMO, including single-task optimization.