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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 42, 2025 No. 2
441-447

Heuristic optimization algorithm based on variable decision level and global learning rate

He Feia
Wang Xiaofenga,b
Tang Aoa
Hua Yingyinga
Peng Qingyuana
Wang Junxiaa
a. School of Computer Science & Engineering, b. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan 750021, China
DOI: 10.19734/j.issn.1001-3695.2024.06.0230

Abstract

Conflict-driven clause learning(CDCL) is the mainstream framework in modern SAT solvers, with branching algorithms based on variable activity being one of the key factors for efficient solving. By combining global learning rate(GLR) and variable decision levels, CDCL obtained two important inferences about its search behavior: when GLR was high, increasing the collision score of low decision level variables reduced search costs; when GLR was low, increasing the collision score of high decision level variables helped explore the solution space. Experimental data confirmed the correctness of these inferences. Based on these inferences, this paper proposed Gdb heuristic strategy combining GLR and variable decision levels to optimize existing branching algorithms. Gdb used two weights, w1 and w2, based on variable decision levels, to adjust variable activity under high and low GLR conditions. The search behavior of the EVSIDS and LRB branching algorithms was also analyzed, and additional weighting was applied to LRB. Experimental results show that the Gdb branching strategy effectively improves the efficiency of CDCL solvers.

Foundation Support

国家自然科学基金资助项目(62062001)
宁夏青年拔尖人才资助项目(2021)
北方民族大学研究生创新项目(YCX23153)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.06.0230
Publish at: Application Research of Computers Printed Article, Vol. 42, 2025 No. 2
Section: Algorithm Research & Explore
Pages: 441-447
Serial Number: 1001-3695(2025)02-016-0441-07

Publish History

[2025-02-05] Printed Article

Cite This Article

何飞, 王晓峰, 唐傲, 等. 结合变量决策层和全局学习率的启发式优化算法 [J]. 计算机应用研究, 2025, 42 (2): 441-447. (He Fei, Wang Xiaofeng, Tang Ao, et al. Heuristic optimization algorithm based on variable decision level and global learning rate [J]. Application Research of Computers, 2025, 42 (2): 441-447. )

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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