Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm
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 so...
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| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/11/1971 |
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| author | Yongjin Lu Kai Li Rui Lin Yunlong Wang Hairong Han |
| author_facet | Yongjin Lu Kai Li Rui Lin Yunlong Wang Hairong Han |
| author_sort | Yongjin Lu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a3c603ca00af4b7ebc7c5109bedf9906 |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-a3c603ca00af4b7ebc7c5109bedf99062025-08-20T02:48:02ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-11-011211197110.3390/jmse12111971Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization AlgorithmYongjin Lu0Kai Li1Rui Lin2Yunlong Wang3Hairong Han4China Ship Development and Design Center, Wuhan 430060, ChinaSchool of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, ChinaChina Ship Development and Design Center, Wuhan 430060, ChinaSchool of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, ChinaChina Ship Development and Design Center, Wuhan 430060, ChinaShip 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.https://www.mdpi.com/2077-1312/12/11/1971ship pipelinegrey wolf optimization (GWO) algorithmpath planningpowell grey wolf optimization (PGWO) algorithm |
| spellingShingle | Yongjin Lu Kai Li Rui Lin Yunlong Wang Hairong Han Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm Journal of Marine Science and Engineering ship pipeline grey wolf optimization (GWO) algorithm path planning powell grey wolf optimization (PGWO) algorithm |
| title | Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm |
| title_full | Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm |
| title_fullStr | Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm |
| title_full_unstemmed | Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm |
| title_short | Intelligent Layout Method of Ship Pipelines Based on an Improved Grey Wolf Optimization Algorithm |
| title_sort | intelligent layout method of ship pipelines based on an improved grey wolf optimization algorithm |
| topic | ship pipeline grey wolf optimization (GWO) algorithm path planning powell grey wolf optimization (PGWO) algorithm |
| url | https://www.mdpi.com/2077-1312/12/11/1971 |
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