RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention
Multiobjective combinatorial optimization (MOCO) problems have a wide range of applications in the real world. Recently, learning-based methods have achieved good results in solving MOCO problems. However, most of these methods use attention mechanisms and their variants, which have room for further...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2025-06-01
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| Series: | Complex System Modeling and Simulation |
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| Online Access: | https://www.sciopen.com/article/10.23919/CSMS.2024.0029 |
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| _version_ | 1849762617634062336 |
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| author | Huiqing Wei Fei Han Qing Liu Henry Han |
| author_facet | Huiqing Wei Fei Han Qing Liu Henry Han |
| author_sort | Huiqing Wei |
| collection | DOAJ |
| description | Multiobjective combinatorial optimization (MOCO) problems have a wide range of applications in the real world. Recently, learning-based methods have achieved good results in solving MOCO problems. However, most of these methods use attention mechanisms and their variants, which have room for further improvement in the speed of solving MOCO problems. In this paper, following the idea of decomposition strategy and neural combinatorial optimization, a novel fast-solving model for MOCO based on retention is proposed. A brand new calculation of retention is proposed, causal masking and exponential decay are deprecated in retention, so that our model could better solve MOCO problems. During model training, a parallel computation of retention is applied, allowing for fast parallel training. When using the model to solve MOCO problems, a recurrent computation of retention is applied, enabling quicker problem-solving. In order to make our model more practical and flexible, a preference-based retention decoder is proposed, which allows generating approximate Pareto solutions for any trade-off preferences directly. An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO. Experimental results show that, while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems. |
| format | Article |
| id | doaj-art-0f15ecc1e90e4eba84724e45906b2768 |
| institution | DOAJ |
| issn | 2096-9929 2097-3705 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Complex System Modeling and Simulation |
| spelling | doaj-art-0f15ecc1e90e4eba84724e45906b27682025-08-20T03:05:42ZengTsinghua University PressComplex System Modeling and Simulation2096-99292097-37052025-06-015212513710.23919/CSMS.2024.0029RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on RetentionHuiqing Wei0Fei Han1Qing Liu2Henry Han3School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, ChinaSchool of Electronic and Information Engineering, West Anhui University, Lu’an 237005, ChinaSchool of Engineering and Computer Science, Baylor University, Waco, TX 76798, USAMultiobjective combinatorial optimization (MOCO) problems have a wide range of applications in the real world. Recently, learning-based methods have achieved good results in solving MOCO problems. However, most of these methods use attention mechanisms and their variants, which have room for further improvement in the speed of solving MOCO problems. In this paper, following the idea of decomposition strategy and neural combinatorial optimization, a novel fast-solving model for MOCO based on retention is proposed. A brand new calculation of retention is proposed, causal masking and exponential decay are deprecated in retention, so that our model could better solve MOCO problems. During model training, a parallel computation of retention is applied, allowing for fast parallel training. When using the model to solve MOCO problems, a recurrent computation of retention is applied, enabling quicker problem-solving. In order to make our model more practical and flexible, a preference-based retention decoder is proposed, which allows generating approximate Pareto solutions for any trade-off preferences directly. An industry-standard deep reinforcement learning algorithm is used to train RM-MOCO. Experimental results show that, while ensuring the quality of problem solving,the proposed method significantly outperforms some other methods in terms of the speed of solving MOCO problems.https://www.sciopen.com/article/10.23919/CSMS.2024.0029multiobjective combinatorial optimizationlearning-based methodretention modeldeep reinforcement learning |
| spellingShingle | Huiqing Wei Fei Han Qing Liu Henry Han RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention Complex System Modeling and Simulation multiobjective combinatorial optimization learning-based method retention model deep reinforcement learning |
| title | RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention |
| title_full | RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention |
| title_fullStr | RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention |
| title_full_unstemmed | RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention |
| title_short | RM-MOCO: A Fast-Solving Model for Neural Multi-Objective Combinatorial Optimization Based on Retention |
| title_sort | rm moco a fast solving model for neural multi objective combinatorial optimization based on retention |
| topic | multiobjective combinatorial optimization learning-based method retention model deep reinforcement learning |
| url | https://www.sciopen.com/article/10.23919/CSMS.2024.0029 |
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