Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm

被引:0
|
作者
Lu, Yongjin [1 ]
Li, Kai [2 ]
Lin, Rui [1 ]
Wang, Yunlong [2 ]
Han, Hairong [1 ]
机构
[1] China Ship Dev & Design Ctr, Wuhan 430060, Peoples R China
[2] Dalian Univ Technol, Sch Naval Architecture & Ocean Engn, Dalian 116024, Peoples R China
关键词
ship pipeline; grey wolf optimization (GWO) algorithm; path planning; powell grey wolf optimization (PGWO) algorithm; ANT COLONY OPTIMIZATION; PIPE; MULTIPLE;
D O I
10.3390/jmse12111971
中图分类号
U6 [水路运输]; P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
Ship piping arrangement is a nondeterministic polynomial problem. Based on the advantages of the grey wolf optimization (GWO) algorithm, which is simple, easy to implement, and has few adjustment parameters and fast convergence speed, the study adopts the grey wolf optimization (GWO) algorithm to solve the ship piping arrangement problem. First, a spatial model of ship piping arrangement is established. The grid cell model and the simplified piping arrangement environment model are established using the raster method. Considering the piping arrangement constraint rules, the mathematical optimization model of piping arrangement is constructed. Secondly, the grey wolf optimization algorithm was optimized and designed. A nonlinear convergence factor adjustment strategy is adopted for its convergence factor. Powell's algorithm is introduced to improve its local search capability, which solves the problem that the grey wolf algorithm easily falls into the local optimum during the solving process. Simulation experiments show that compared with the standard grey wolf algorithm, the improved algorithm can improve the path layout effect by 38.03% and the convergence speed by 36.78%. The improved algorithm has better global search ability, higher solution stability, and faster convergence speed than the standard grey wolf optimization algorithm. At the same time, the algorithm is applied to the actual ship design, and the results meet the design expectations. The improved algorithm can be used for other path-planning problems.
引用
收藏
页数:21
相关论文
共 50 条
  • [21] Parameters identification of magnetorheological damper based on improved grey wolf optimization algorithm
    Li, Kangjun
    Yang, Xiaolong
    Li, Denghui
    Xie, Guojin
    PHYSICA SCRIPTA, 2025, 100 (01)
  • [22] Path Planning of UAV Based on Improved Adaptive Grey Wolf Optimization Algorithm
    Zhang, Wei
    Zhang, Sai
    Wu, Fengyan
    Wang, Yagang
    IEEE ACCESS, 2021, 9 : 89400 - 89411
  • [23] Dynamic reconfiguration of a distribution network based on an improved grey wolf optimization algorithm
    Tian S.
    Liu L.
    Wei S.
    Fu Y.
    Mi Y.
    Liu S.
    Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control, 2021, 49 (16): : 1 - 11
  • [24] Logistics Job Intelligent Scheduling Model Based on Discrete Grey Wolf Optimization Algorithm
    Gao, Tian-Juan
    Zhou, Yuan
    Masurat, Thomas
    Journal of Network Intelligence, 2024, 9 (02): : 850 - 864
  • [25] Improved Grey Wolf Optimization Algorithm for Overcurrent Relays Coordination
    Jamal, Noor Zaihah
    Sulaiman, Mohd Herwan
    Aliman, Omar
    Mustaffa, Zuriani
    Mustafa, Mohd Wazir
    2018 9TH IEEE CONTROL AND SYSTEM GRADUATE RESEARCH COLLOQUIUM (ICSGRC2018), 2018, : 7 - 12
  • [26] Application of an Improved Grey Wolf Optimization Algorithm in Path Planning
    Xiao, Ping
    Jin, Kai
    Liu, Youyu
    PROCEEDINGS OF 2024 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND DIGITAL APPLICATIONS, MIDA2024, 2024, : 331 - 338
  • [27] A nutrient optimization method for hydroponic lettuce based on multi-strategy improved grey wolf optimizer algorithm
    Zhang, Xihai
    Xia, Juheng
    Chen, Zerui
    Zhu, Jiaxi
    Wang, Hao
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2024, 224
  • [28] A Feature Selection Method of Parallel Grey Wolf Optimization Algorithm Based on Spark
    Chen, Hongwei
    Han, Lin
    Hu, Zhou
    Hou, Qiao
    Ye, Zhiwei
    Zeng, Jun
    Yuan, Jiansen
    PROCEEDINGS OF THE 2019 10TH IEEE INTERNATIONAL CONFERENCE ON INTELLIGENT DATA ACQUISITION AND ADVANCED COMPUTING SYSTEMS - TECHNOLOGY AND APPLICATIONS (IDAACS), VOL. 1, 2019, : 81 - 85
  • [29] Estimation of Multiple Parameters in Semitransparent Mediums Based on an Improved Grey Wolf Optimization Algorithm
    Li, Kefu
    Xie, Lang
    Zhou, Jianhua
    Wu, Xiaofang
    Ding, Ding
    Li, Caibin
    PROCESSES, 2024, 12 (07)
  • [30] Research on Dynamic Economic Dispatch Optimization Problem Based on Improved Grey Wolf Algorithm
    Yang, Wenqiang
    Zhang, Yihang
    Zhu, Xinxin
    Li, Kunyan
    Yang, Zhile
    ENERGIES, 2024, 17 (06)