JCSAI | Journal of Computer Science and Artificial Intelligence
← Back to Volume 3, Issue 3

Application of the Naozhou Population of Larimichthys Crocea School Algorithm (DNPFS-OA) in Path Planning for AUV Clusters

Chang Liu, Rongjie Cai, Zhiwen Yu, Yingzhi Ma, Xin Guo
Vol. 3, No. 3, pp. 46-53, 2025 DOI: 10.54097/t7z59w44 July 21, 2025
Download PDF
Abstract.Aiming at the cooperative path planning problem of AUV cluster in underwater complex environment, this paper proposes a deep distributed optimization algorithm (DNPFS-OA) based on the intelligent behavior of Tanzhou Pseudosciaena crocea. This algorithm is different from the traditional leader-follower architecture. By establishing the deep coupling between the bio-pressure sensing model and the hydrodynamic equation, and combining with the communication-control collaborative optimization framework, the autonomous collaboration of AUV clusters in the three-dimensional strong disturbance environment is realized. The sea test shows that the path length of DNPFS-OA is reduced by 28.3%, the energy consumption is reduced by 35.7%, and the mapping efficiency is improved by 42.6% when the communication packet loss rate is 30%.
Keywords. Autonomous Underwater Vehicle Swarm Bionic Algorithm Path Planning
APA Citation Chang Liu, Rongjie Cai, Zhiwen Yu, Yingzhi Ma, Xin Guo (2025). Application of the Naozhou Population of Larimichthys Crocea School Algorithm (DNPFS-OA) in Path Planning for AUV Clusters. Journal of Computer Science and Artificial Intelligence, 3(3), 46-53. https://doi.org/10.54097/t7z59w44

References

  1. Ongming Cheng, Mingyan Jiang,Dongfeng Yuan. Novel Clustering Algorithms Based on Improved Artificial Fish Swarm Algorithm [A]. 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery [C]. Tianjin, China: IEEE, 2009:141-145P.
  2. Fletcher B.UUV Master Plan: a Vision for Navy UUV Development [J]. Oceans 2000 MTS/IEEE.2000(1):65-71P.
  3. HongyanShi, ZhaoyuBei. A Mixed Ant Colony Algorithm for Function Optimization [A]. Proceedings-4th International Conference on Natural Computation [C]. Guilin, China: IEEE, 2008:284-286P.
  4. Mingyan Jiang, Nikos E. Mastorakis, Dongfeng Yuan. Image Segmentation with Improved Artificial Fish Swarm Algorithm [A]. Proceedings of the European Computing Conference [C]. 2009, 28(2):133-138P.
  5. Yan Wang, Liguo Zhang. Method of Bayesian Network Parameter Learning Base on Improved Artificial Fish Swarm Algorithm [A]. 2010 International Conference on Bio- inspired Systems and Signal Processing [C]. Wuhan, China: IEEE, 2010:147-149P.