App. Res. of Comp. Accepted Paper
Vol. 44, 2027 No. 2

Adaptive dimension learning and dynamic opposition-based learning for multitask optimization

Wang Haixiang1
Wu Zebin2
Wang Zijia2
1. College of Information Engineering, Kaifeng University, Kaifeng Henan 475000, China
2. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China
DOI: 10.19734/j.issn.1001-3695.2026.06.0160

Abstract

To address knowledge transfer restricted to strictly aligned dimensions, underutilization of information from misaligned dimensions, negative transfer, and late-stage local optima in multitask optimization, this study developed an adaptive dimension learning and dynamic opposition-based learning algorithm for multitask optimization (ADOMTO) . The adaptive dimension learning strategy constructed a dimension correspondence matrix for intra-task and inter-task mappings, switched among intra-task aligned learning, intra-task reordered learning, and inter-task transfer based on historical improvements, and exploited transferable knowledge from misaligned dimensions. The dynamic opposition-based learning strategy adjusted the stagnation threshold during evolution and applied quasi-opposition-based learning to stagnant individuals to escape local optima. Compared with OTMTO, BLKT-DE, MMLMTO, and other state-of-the-art algorithms, ADOMTO achieved at least 12 wins and at most five losses on CEC17-MTSO, and at least 14 wins and at most four losses on CEC22-MTSO. ADOMTO exploits transferable knowledge from misaligned dimensions, mitigates negative transfer and search stagnation, and maintains high accuracy and robustness.

Foundation Support

广东省自然科学基金资助项目(2025A1515010256,2026A1515011523,2026A1515060004)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.06.0160
Publish at: Application Research of Computers Accepted Paper, Vol. 44, 2027 No. 2

Publish History

[2026-10-21] Accepted Paper

Cite This Article

王海翔, 吴泽彬, 王子佳. 自适应维度学习和动态反向学习的多任务优化 [J]. 计算机应用研究, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0160. (Wang Haixiang, Wu Zebin, Wang Zijia. Adaptive dimension learning and dynamic opposition-based learning for multitask optimization [J]. Application Research of Computers, 2027, 44 (2). (2026-10-24). https://doi.org/10.19734/j.issn.1001-3695.2026.06.0160. )

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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