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

Multi-task differential evolution algorithm with hybrid knowledge transfer

Wang Jiaxun1,2
Li Xi1,2
Feng Yanhong1,2
1. School of Information Engineering, Hebei GEO University, Shijiazhuang 052161, China
2. Intelligent Sensor Network Engineering Research Center of Hebei Province, Hebei GEO University, Shijiazhuang 052161, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0449

Abstract

Multi-task optimisation represents a novel paradigm within the field of evolutionary computation. Addressing the limitation that existing algorithms predominantly transfer knowledge in singular forms, often struggling to fully leverage task interdependencies when confronted with task combinations exhibiting varying degrees of similarity and complex interrelationships, this paper proposes a hybrid knowledge transfer-based multi-task differential evolutionary algorithm (HKT-MTDE) . The algorithm employs two distinct knowledge carriers: individuals and mutation strategies. First, a designed hybrid knowledge transfer mechanism: individual transfer facilitates the sharing of solution structures across tasks by propagating the encoded information of high-fitness individuals; strategy transfer collects performance data from seven differential evolution mutation strategies, establishing a statistical model of strategy success rates to transfer efficient search strategies, thereby promoting the collaborative utilisation of optimisation experience across tasks. Second, an adaptive parameter adjustment mechanism controls the degree of transfer, dynamically adjusting mutation factors, crossover rates, and transfer probabilities based on strategy success rates and population evolutionary progress. Finally, experimental results on the CEC17-MTSO and WCCI20-MTSO benchmark datasets demonstrate that HKT-MTDE outperforms state-of-the-art algorithms in most problems. Furthermore, experiments on planar kinematic arm control problem showcase the algorithm's superior performance in practical application scenarios.

Foundation Support

河北省高校科技研究项目(ZD2022083)
河北地质大学国家预研项目(KY2024YB06)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0449
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 7

Publish History

[2026-03-18] Accepted Paper

Cite This Article

王嘉旬, 李晰, 冯艳红. 混合知识迁移的多任务差分演化算法 [J]. 计算机应用研究, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0449. (Wang Jiaxun, Li Xi, Feng Yanhong. Multi-task differential evolution algorithm with hybrid knowledge transfer [J]. Application Research of Computers, 2026, 43 (7). (2026-03-24). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0449. )

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  • 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.

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