On the use of transfer learning in nature-inspired algorithms: a systematic review

Abstract Transfer Learning (TL) has gained significant traction in machine learning, especially in deep learning contexts. However, its integration with Nature-Inspired Algorithms (NIAs) remains fragmented, with limited understanding of strategies, challenges, and outcomes. This paper presents the first systematic review focused exclusively on the use of TL in NIAs, excluding deep learning approaches. Major challenges include dealing with domain/task similarity, avoiding negative transfer, selecting what and when to transfer, and adapting TL mechanisms to population-based search paradigms. To address these issues, we conducted a structured analysis of 47 primary studies, categorizing them by TL strategies, learning paradigms, and algorithmic goals. Our findings reveal recurring patterns, highlight open research gaps, and propose future directions for developing robust TL-based NIAs. This review provides a foundation for researchers interested in designing adaptive, efficient, and knowledge-guided metaheuristics for complex optimization tasks.