Abstract
Weighted vertex cover (WVC) problem, as one of the most important combinatorial optimization problems, is a general form of the classic vertex cover problem. The existing researches focused on the solution quality but ignoring the time efficiency, which makes their tradeoff be a technical challenge. In this paper, we first reformulate the WVC problem as an asymmetric snowdrift game on weighted networks. Then, we propose a new game-based algorithm by combining temporal difference method in reinforcement learning, that is, the temporal-difference(λ)-based game algorithm (TD(λ)-GA), which can achieve a balance between the running time and the solution quality. Finally, we perform numerical simulations to demonstrate that the proposed algorithm can find a high-quality solution within reasonable runtime by comparing it with other representative optimization algorithms. © 2026 IEEE.
| Original language | English |
|---|---|
| Pages (from-to) | 4448-4463 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Artificial Intelligence |
| Volume | 7 |
| Issue number | 8 |
| Online published | 16 Feb 2026 |
| DOIs | |
| Publication status | Published - Aug 2026 |
Funding
This work was supported in part by the National Science and Technology Major Project of China under Grant 2022ZD0116900, the National Natural Science Foundation of China under Grant 62173308 and the Natural Science Foundation of Zhejiang Province of China under Grant LZ24F030009.
Research Keywords
- asymmetric snowdrift game
- game-based algorithm
- reinforcement learning
- temporal difference
- Weighted vertex cover
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