Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization
<|reference_start|>Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive Optimization: Expensive optimization problems (EOPs) have attracted increasing research attention over the decades due to their ubiquity in a variety of practical applications. Despite many sophisticated surrogate-assisted evolutionary algorithms (SAEAs) that have been developed for solving such problems, most of them lack the ability to transfer knowledge from previously-solved tasks and always start their search from scratch, making them troubled by the notorious cold-start issue. A few preliminary studies that integrate transfer learning into SAEAs still face some issues, such as defective similarity quantification that is prone to underestimate promising knowledge, surrogate-dependency that makes the transfer methods not coherent with the state-of-the-art in SAEAs, etc. In light of the above, a plug and play competitive knowledge transfer method is proposed to boost various SAEAs in this paper. Specifically, both the optimized solutions from the source tasks and the promising solutions acquired by the target surrogate are treated as task-solving knowledge, enabling them to compete with each other to elect the winner for expensive evaluation, thus boosting the search speed on the target task. Moreover, the lower bound of the convergence gain brought by the knowledge competition is mathematically analyzed, which is expected to strengthen the theoretical foundation of sequential transfer optimization. Experimental studies conducted on a series of benchmark problems and a practical application from the petroleum industry verify the efficacy of the proposed method. The source code of the competitive knowledge transfer is available at https://github.com/XmingHsueh/SAS-CKT.<|reference_end|>
@article{xue2024surrogate-assisted,
    title={Surrogate-Assisted Search with Competitive Knowledge Transfer for
  Expensive Optimization},
    author={Xiaoming Xue, Yao Hu, Liang Feng, Kai Zhang, Linqi Song, Kay Chen Tan},
    journal={arXiv preprint arXiv:2408.07176},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07176},
    primaryClass={cs.NE}
}