Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in parallel. Accordingly, a broad range of knowledge transfer methods (KTMs) have been developed as integral components of EMTO algorithms, most of which are tailored to specific problem settings. However, the design of effective KTMs typically relies on substantial domain expertise and careful manual customization, as different EMTO scenarios require distinct transfer strategies to achieve performance gains. Meanwhile, recent advances in large language models (LLMs) have demonstrated strong capabilities in autonomous programming and algorithm synthesis, opening up new possibilities for automating the design of optimization solvers. Motivated by this, in this paper, we propose a Self-guided Knowledge Transfer Design (SKTD) framework that leverages LLMs to autonomously generate knowledge transfer methods (KTMs) as algorithmic components within EMTO. By enabling data-driven and self-adaptive construction of transfer strategies, SKTD facilitates effective knowledge reuse across heterogeneous tasks and diverse EMTO scenarios. To the best of our knowledge, this work represents the first attempt to automate the generation of KTMs for EMTO. Extensive experiments on well-established EMTO benchmarks with varying degrees of task similarity demonstrate that the proposed SKTD consistently achieves superior or highly competitive performance compared with both the state-of-the-art program search approach and manually designed EMTO methods, in terms of optimization effectiveness and cross-scenario generalization.

翻译：进化多任务优化（EMTO）是一种先进的优化范式，通过支持并行求解多个任务间的知识迁移来提高搜索效率。为此，研究者已开发出多种作为EMTO算法核心组件的知识迁移方法（KTMs），其中大多针对特定问题场景定制。然而，设计有效的KTM通常依赖大量领域知识与精细的人工定制，因为不同EMTO场景需要不同的迁移策略才能实现性能提升。与此同时，大语言模型（LLMs）的最新进展已展现出在自主编程和算法合成方面的强大能力，为自动化设计优化求解器开辟了新可能。受此启发，本文提出了一种自引导知识迁移设计框架（SKTD），利用LLMs在EMTO中自主生成作为算法组件的知识迁移方法（KTMs）。通过实现数据驱动且自适应构建的迁移策略，SKTD促进了异构任务及不同EMTO场景间有效的知识复用。据我们所知，本工作是首次尝试自动化生成EMTO的KTMs。在任务相似度各异的成熟EMTO基准测试上进行的大量实验表明，与最先进的程序搜索方法和人工设计的EMTO方法相比，所提出的SKTD在优化效果和跨场景泛化能力方面始终表现出优越或极具竞争力的性能。