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App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 3

Multi-objective multi-task evolutionary algorithm with dynamic knowledge selection and adaptive transfer

Li Tao1,2
Ma Haoyue1
Li Xingchen1
Xu Jiucheng1,2
1. College of Computer and Information Engineering, Henan Normal University, Xinxiang Henan 453007, China
2. Engineering Lab of He'nan Province for Intelligence Business & Internet of Things, Xinxiang Henan 453007, China
DOI: 10.19734/j.issn.1001-3695.2025.08.0275

Abstract

Multi-objective and multi-task optimization (MO-MTO) aims to simultaneously optimize a set of multi-objective optimization problems by transferring search experience across tasks. To address the phenomenon of negative knowledge transfer during problem solving, this paper proposes a multi-objective multi-task evolutionary algorithm with dynamic knowledge selection and adaptive transfer (MTEA-DSAT) . First, a population generation method based on neighborhood collaboration was presented to explore new solution space regions through minimal knowledge transfer, thereby mitigating blind search in subproblems. Second, a dynamic knowledge selection and adaptive transfer mechanism was designed. It adapted the knowledge type and transfer probability based on the evolutionary state of subproblems to reduce negative transfer. Finally, a history-driven neighborhood update strategy was designed to enhance robustness by leveraging historical information to dynamically adjust the neighborhood structure of search direction and position information. Experimental results show that the proposed algorithm exhibits good convergence and competitiveness on both the test suite CEC21-CPLX and optimal power flow problems.

Foundation Support

国家自然科学基金资助项目(62406104)
河南省高等学校青年骨干教师培养计划(2025GGJS033)
河南省高等学校重点科研项目基础研究计划(25A520029)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.08.0275
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 3

Publish History

[2025-11-18] Accepted Paper

Cite This Article

李涛, 马昊月, 李兴晨, 等. 动态知识选择与自适应迁移的多目标多任务进化算法 [J]. 计算机应用研究, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0275. (Li Tao, Ma Haoyue, Li Xingchen, et al. Multi-objective multi-task evolutionary algorithm with dynamic knowledge selection and adaptive transfer [J]. Application Research of Computers, 2026, 43 (3). (2025-11-18). https://doi.org/10.19734/j.issn.1001-3695.2025.08.0275. )

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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