Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
146-150

Deep reinforcement learning algorithm for rectangle layout optimization with equilibrium constraint

Xu Yichun
Wan Shuzhen
Dong Fangmin
College of Computer & Information Technology, China Three Gorges University, Yichang Hubei 443002, China
DOI: 10.19734/j.issn.1001-3695.2021.05.0218

Abstract

The rectangle layout optimization problem with balance constraints is derived from the layout design of satellite module equipment. It belongs to combinatorial optimization problem. Deep reinforcement learning uses reward mechanism to realize high-performance decision optimization through data training. This paper proposed a new deep reinforcement learning algorithm DAR and its extension IDAR. The DAR algorithm output the optimized location sequence with a pointer network, and then used the positioning mechanism to give the layout results. The training of pointer network was realized by deep reinforcement learning. The time complexity of the DAR algorithm was O(n3). The IDAR algorithm was an iteration version of DAR, which had better results but with a time complexity of O(n4). The test results show that DAR algorithm has good lear-ning ability, and the model obtained by small layout problems can be effectively applied to large-scale problems. The results on two typical large-scaled instances show that the proposed algorithms have achieved or approached to the current best results, so that they have advantages both in time and solution quality.

Foundation Support

国家自然科学基金—新疆联合基金资助项目(U1703261)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.05.0218
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Algorithm Research & Explore
Pages: 146-150
Serial Number: 1001-3695(2022)01-026-0146-05

Publish History

[2021-10-08] Accepted Paper
[2022-01-05] Printed Article

Cite This Article

徐义春, 万书振, 董方敏. 带平衡约束矩形布局优化问题的深度强化学习算法 [J]. 计算机应用研究, 2022, 39 (1): 146-150. (Xu Yichun, Wan Shuzhen, Dong Fangmin. Deep reinforcement learning algorithm for rectangle layout optimization with equilibrium constraint [J]. Application Research of Computers, 2022, 39 (1): 146-150. )

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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