App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Deep adaptive inference model for fish schooling motion under threatening environments

Liu Leia,b
Ma Jingb
a. School of Management, b. School of Optoelectronics, University of Shanghai for Science and Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2026.02.0003

Abstract

Adaptive modeling of biological collectives in dynamic and complex environments provides a core approach for understanding self-organization and for advancing artificial multi-agent systems. To address the difficulty of conventional deep learning models in coping with unknown disturbances due to fixed parameters during inference, this paper proposes a deep learning model based on adaptive inference. The model embeds a Test-Time Adaptation (TTA) module to refine online features of neighboring agents during inference, and applies a hierarchical attention mechanism to structurally decouple environmental features. These components together form a deep adaptive inference network, termed the Test-Time-Adaptation Transformer (TTA-Former) . This study built a dedicated fish–robot hybrid experimental platform to collect behavioral data for training the proposed model. Simulation results show that TTA-Former achieves favorable performance on macroscopic indicators such as school polarization and aggregation, and maintains strong generalization ability and environmental adaptability across groups of different sizes as well as in complex scenarios involving static obstacles and dynamic threats. The proposed model demonstrates considerable potential for self-organized modeling and control of complex systems, and offers a new design perspective for developing more resilient unmanned swarm systems.

Foundation Support

国家自然科学基金资助项目(72071130)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.02.0003
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-22] Accepted Paper

Cite This Article

刘磊, 马婧. 威胁环境下鱼群运动的深度适应性推理模型 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0003. (Liu Lei, Ma Jing. Deep adaptive inference model for fish schooling motion under threatening environments [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2026.02.0003. )

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.

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