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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 4

Gradient descent learning based on reinforcement learning strategy for solving GCP problem

Song Jiahuan1
Wang Xiaofeng1,2
Hu Simin1
Yao Jiaxing1
Suo Xiaona1
1. School of Computer Science & Engineering, North Minzu University, Yinchuan 750021, China
2. Laboratory of Image & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0330

Abstract

The Graph Coloring Problem (GCP) is a classical combinatorial optimization problem that aims to assign different colors to each vertex in a graph, ensuring that adjacent vertices have different colors while minimizing the total number of colors used. As an NP-hard problem, GCP presents challenges for traditional solution methods, such as greedy algorithms, heuristic search, and evolutionary algorithms, which are often limited by high computational complexity and a tendency to get trapped in local optima. To address these issues, this paper proposes a gradient descent learning method based on Reinforcement Learning Strategies (RLS) for solving GCP. Specifically, we reformulate GCP as a policy optimization problem within the reinforcement learning framework, designing a policy gradient algorithm that maps graph coloring states to reinforcement learning states, treats color assignments as actions, and uses the negative objective function value as a reward signal to iteratively optimize the coloring strategy. Experimental results demonstrate that the proposed method outperforms conventional heuristic algorithms across various types and scales of graph instances, showing strong global exploration capabilities and convergence, especially in high-dimensional and complex constraint scenarios. This study suggests that the reinforcement learning-based approach to graph coloring holds broad potential for complex combinatorial optimization problems, offering an effective new solution pathway for GCP and related problems.

Foundation Support

国家自然科学基金项目(62062001)
宁夏青年拔尖人才项目(2021)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0330
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 4

Publish History

[2024-12-25] Accepted Paper

Cite This Article

宋家欢, 王晓峰, 胡思敏, 等. 基于强化学习策略的梯度下降学习求解GCP问题 [J]. 计算机应用研究, 2025, 42 (4). (2024-12-31). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0330. (Song Jiahuan, Wang Xiaofeng, Hu Simin, et al. Gradient descent learning based on reinforcement learning strategy for solving GCP problem [J]. Application Research of Computers, 2025, 42 (4). (2024-12-31). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0330. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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