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RM-MOCO:A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention

Huiqing Wei Fei Han Qing Liu Henry Han

复杂系统建模与仿真（英文）2025，Vol.5Issue(2)：125-137,13.

复杂系统建模与仿真（英文）2025，Vol.5Issue(2)：125-137,13.DOI:10.23919/CSMS.2024.0029

RM-MOCO:A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention

Huiqing Wei ¹Fei Han ¹Qing Liu ²Henry Han³

作者信息

1. School of Computer Science and Communication Engineering,Jiangsu University,Zhenjiang 212013,China
2. School of Electronic and Information Engineering,West Anhui University,Lu'an 237005,China
3. School of Engineering and Computer Science,Baylor University,Waco,TX 76798,USA
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摘要

关键词

multiobjective combinatorial optimization/learning-based method/retention model/deep reinforcement learning

Key words

multiobjective combinatorial optimization/learning-based method/retention model/deep reinforcement learning

引用本文复制引用

Huiqing Wei,Fei Han,Qing Liu,Henry Han..RM-MOCO:A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention[J].复杂系统建模与仿真（英文）,2025,5(2):125-137,13.

基金项目

This work was supported by the National Natural Science Foundation of China(No.62102002). （No.62102002）

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