App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 5

Multi origin collaborative reasoning driven by deep reinforcement learning in neural networks application in combinatorial optimization

Li Chengjian
Song Shuyi
Chen Zhibin
Faculty of Science, Kunming University of Science and Technology, Kunming 650500, China
DOI: 10.19734/j.issn.1001-3695.2025.09.0388

Abstract

This paper addresses the challenges of insufficient solution-set diversity and limited global search capability in multi-objective combinatorial optimization by proposing a multi-start neural combinatorial optimization framework, MONCO, which is developed to enhance the performance and generalization ability of deep learning methods on multi-objective combinatorial optimization tasks. The proposed framework integrates a multi-start inference mechanism, a reinforcement learning strategy, and instance augmentation techniques to enable parallel exploration of multiple solutions, and introduces the Das and Dennis decomposition strategy to achieve a more uniform coverage of the objective space, thereby better approximating the Pareto front. This paper evaluates MONCO on three representative multi-objective problems, MOTSP, MOCVRP, and MOKP, and the results show that MONCO achieves overall better solution quality and cross-scale adaptability than the representative deep learning-based and traditional optimization methods selected in this study. On the Bi-TSP100 problem, MONCO-Aug attains an HV value of 0.682 with a 0% gap, markedly outperforming NSGA-II and MOEA/D; on the Bi-CVRP200 problem, MONCO-Aug achieves an HV of 0.347, representing an improvement of approximately 47% over the 0.236 obtained by P-MOCO-Aug. These results verify the effectiveness of the proposed framework in improving both the diversity and convergence of the solution set, and demonstrate its stable performance and adaptability across different problem scales, indicating that MONCO constitutes a promising direction for advancing deep learning–based research in multi-objective combinatorial optimization.

Foundation Support

国家自然科学基金资助项目(12361065,11761042)
人工智能赋能典籍翻译与传播创新团队资助项目(KGZSCXTD2025004)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.09.0388
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 5

Publish History

[2026-01-18] Accepted Paper

Cite This Article

李成健, 宋姝谊, 陈智斌. 深度强化学习驱动的多起点协同推理在神经组合优化中的应用 [J]. 计算机应用研究, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0388. (Li Chengjian, Song Shuyi, Chen Zhibin. Multi origin collaborative reasoning driven by deep reinforcement learning in neural networks application in combinatorial optimization [J]. Application Research of Computers, 2026, 43 (5). (2026-01-20). https://doi.org/10.19734/j.issn.1001-3695.2025.09.0388. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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