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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 5

Constrained multi-objective task offloading optimization method based on weight networks

Zhao Wenzhuoa,b
Tang Honga,b
Feng Chena,b
Dai Jiayua,b
a. School of Communications & Information Engineering, b. Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2024.09.0356

Abstract

In the existing research on mobile edge computing (MEC) networks, a considerable portion of existing studies has mainly focused on optimizing either delay or energy consumption separately. However, in practical computing offloading scenarios, conflicting objectives directly impact and constrain user experience, making it challenging to optimize a single objective to meet complex application requirements. Moreover, some attempts to combine multiple optimization objectives into one through weighted sums often lead to the loss of target requirement information. To overcome these issues, the authors propose the WNDQN method. By introducing a weight network, the decision center can select the most relevant weight value based on environmental information and execute agent - recommended actions. Additionally, integrating experience replay and action exploration mechanisms based on loss value prevents convergence to local optima, accelerates convergence, and improves stability. Experimental results confirm that our method accelerates neural network convergence, balances delay and energy consumption, reduces weighted costs for edge clients, and enhances user experience under limited battery power.

Foundation Support

国家自然科学基金资助项目(61971080)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.09.0356
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 5

Publish History

[2025-03-06] Accepted Paper

Cite This Article

赵文卓, 唐宏, 冯琛, 等. 一种基于权重网络的约束多目标任务卸载优化方法 [J]. 计算机应用研究, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0356. (Zhao Wenzhuo, Tang Hong, Feng Chen, et al. Constrained multi-objective task offloading optimization method based on weight networks [J]. Application Research of Computers, 2025, 42 (5). (2025-03-06). https://doi.org/10.19734/j.issn.1001-3695.2024.09.0356. )

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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