Adaptive dual-space discriminator for large-scale multi-objective optimization
- 1College of Computer Science and Artificial Intelligence, Southwest Minzu University
Abstract
Numerous real-world applications involve large-scale multi-objective optimization problems (LSMOPs) with hundreds or even thousands of decision variables. Although multi-objective evolutionary algorithms (MOEAs) are effective for such tasks, relying solely on objective-space information often provides insufficient selection pressure in high-dimensional settings. To address this issue, an adaptive dual-space discriminator is proposed to coordinate selection strategies from both the objective and decision spaces through a state-aware adaptive operator-selection method with a recurrent selector based on direct population-improvement feedback, and a corresponding algorithm termed MOEA-RLD is developed by integrating dual-space selection, online strategy adaptation, a bounded population pool, and APD environmental selection. Comparative evaluations on the LSMOP benchmark suite against five representative large-scale multi-objective evolutionary algorithms show that MOEA-RLD achieves favorable average rankings across the tested settings. Further evaluations on three real-world application settings provide evidence of both the practical performance of the proposed method and its limitations.
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BibTeX
@article{xue2026adaptive,
title = {{Adaptive dual-space discriminator for large-scale multi-objective optimization}},
author = {Ziyi Xue and Kaidong Cheng and Xunze Liu and Yingjie Li and Gang Xin},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-70129-w},
url = {https://doi.org/10.1038/s41598-026-70129-w},
note = {CC BY},
}
RIS
TY - JOUR
TI - Adaptive dual-space discriminator for large-scale multi-objective optimization
AU - Ziyi Xue
AU - Kaidong Cheng
AU - Xunze Liu
AU - Yingjie Li
AU - Gang Xin
PY - 2026
DA - 2026/09/01
JO - Scientific Reports
DO - 10.1038/s41598-026-70129-w
UR - https://doi.org/10.1038/s41598-026-70129-w
AB - Numerous real-world applications involve large-scale multi-objective optimization problems (LSMOPs) with hundreds or even thousands of decision variables. Although multi-objective evolutionary algorithms (MOEAs) are effective for such tasks, relying solely on objective-space information often provides insufficient selection pressure in high-dimensional settings. To address this issue, an adaptive dual-space discriminator is proposed to coordinate selection strategies from both the objective and decision spaces through a state-aware adaptive operator-selection method with a recurrent selector based on direct population-improvement feedback, and a corresponding algorithm termed MOEA-RLD is developed by integrating dual-space selection, online strategy adaptation, a bounded population pool, and APD environmental selection. Comparative evaluations on the LSMOP benchmark suite against five representative large-scale multi-objective evolutionary algorithms show that MOEA-RLD achieves favorable average rankings across the tested settings. Further evaluations on three real-world application settings provide evidence of both the practical performance of the proposed method and its limitations.
SN - 2045-2322
ER -