{"ID":2849672,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23407","arxiv_id":"2510.23407","title":"Multi-Task Surrogate-Assisted Search with Bayesian Competitive Knowledge Transfer for Expensive Optimization","abstract":"Expensive optimization problems (EOPs) present significant challenges for traditional evolutionary optimization due to their limited evaluation calls. Although surrogate-assisted search (SAS) has become a popular paradigm for addressing EOPs, it still suffers from the cold-start issue. In response to this challenge, knowledge transfer has been gaining popularity for its ability to leverage search experience from potentially related instances, ultimately facilitating head-start optimization for more efficient decision-making. However, the curse of negative transfer persists when applying knowledge transfer to EOPs, primarily due to the inherent limitations of existing methods in assessing knowledge transferability. On the one hand, a priori transferability assessment criteria are intrinsically inaccurate due to their imprecise understandings. On the other hand, a posteriori methods often necessitate sufficient observations to make correct inferences, rendering them inefficient when applied to EOPs. Considering the above, this paper introduces a Bayesian competitive knowledge transfer (BCKT) method developed to improve multi-task SAS (MSAS) when addressing multiple EOPs simultaneously. Specifically, the transferability of knowledge is estimated from a Bayesian perspective that accommodates both prior beliefs and empirical evidence, enabling accurate competition between inner-task and inter-task solutions, ultimately leading to the adaptive use of promising solutions while effectively suppressing inferior ones. The effectiveness of our method in boosting various SAS algorithms for both multi-task and many-task problems is empirically validated, complemented by comparative studies that demonstrate its superiority over peer algorithms and its applicability to real-world scenarios. The source code of our method is available at https://github.com/XmingHsueh/MSAS-BCKT.","short_abstract":"Expensive optimization problems (EOPs) present significant challenges for traditional evolutionary optimization due to their limited evaluation calls. Although surrogate-assisted search (SAS) has become a popular paradigm for addressing EOPs, it still suffers from the cold-start issue. In response to this challenge, kn...","url_abs":"https://arxiv.org/abs/2510.23407","url_pdf":"https://arxiv.org/pdf/2510.23407v1","authors":"[\"Yi Lu\",\"Xiaoming Xue\",\"Kai Zhang\",\"Liming Zhang\",\"Guodong Chen\",\"Chenming Cao\",\"Piyang Liu\",\"Kay Chen Tan\"]","published":"2025-10-27T15:09:31Z","proceeding":"cs.NE","tasks":"[\"cs.NE\"]","methods":"[]","has_code":false,"code_links":[{"ID":607730,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2849672,"paper_url":"https://arxiv.org/abs/2510.23407","paper_title":"Multi-Task Surrogate-Assisted Search with Bayesian Competitive Knowledge Transfer for Expensive Optimization","repo_url":"https://github.com/XmingHsueh/MSAS-BCKT","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}