App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 7

Hierarchical multi-objective multi-task optimization algorithm

Li Erchao
Guo Qi
Peng Yu
College of Automation and Electrical Engineering, Lanzhou University of Technology, Lanzhou 730050, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0463

Abstract

This study addresses the limitations of coarse-grained knowledge transfer mechanisms in multi-objective multi-task optimization, which often cause negative transfer risks and lack adaptability to dynamic requirements. The proposed algorithm HMOA introduces a hierarchical hybrid transfer strategy featuring three key innovations. First, it constructs a hierarchical architecture that decouples decision variables into shared and independent layers, enabling collaborative optimization of common and specific characteristics. Second, it designs a hybrid transfer mechanism that dynamically switches between direct transfer and model normalization based on real-time task similarity calculations. Third, it implements a dynamic parameter adjustment strategy that regulates migration rates according to fitness feedback. The evaluation demonstrates HMOA's superior performance through comprehensive testing on 30 benchmark MO-MTO problems. Comparative experiments with nine state-of-the-art MO-MTEAs and three MOEA algorithms show significant advantages in convergence and diversity metrics. Ablation studies confirm the individual contribution of each strategy, while practical validation using a multi-objective sensor coverage problem proves its effectiveness in real-world applications. Experimental results establish that HMOA achieves better convergence speed and solution diversity compared to existing approaches. The algorithm provides an efficient solution for complex MO-MTO scenarios by effectively balancing knowledge transfer efficiency with specificity preservation. Its innovative architecture offers new possibilities for handling challenging multi-task optimization problems.

Foundation Support

甘肃省自然科学基金重点项目(24JRRA173)
甘肃省科技计划重点研发计划项目(25YFGA030)
甘肃省优秀博士生项目(24JRRA205)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0463
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-25] Accepted Paper

Cite This Article

李二超, 郭启, 彭钰. 分层混合迁移策略的多目标多任务进化算法 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0463. (Li Erchao, Guo Qi, Peng Yu. Hierarchical multi-objective multi-task optimization algorithm [J]. Application Research of Computers, 2026, 43 (7). (2026-03-27). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0463. )

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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