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.
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收藏
页数:21
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