Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm

LI Zhe; WU Jun; WANG Shunsen; LI Yong; YAN Xiaojiang; CHENG Shangfang; LIU Guanwei

doi:10.7652/xjtuxb202306019

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Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm

更新时间：2025-05-08

- Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm
- Vol. 57, Issue 6, Pages: 172-180(2023)
- 作者机构：
  
  1. 西安交通大学叶轮机械研究所,西安,710049
  2. 武汉第二船舶设计研究所热能动力技术重点实验室,武汉,430205
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202306019
  CLC： TP39;TP181
- Online First：10 June 2023，
  
  Published：2023
- 稿件说明：
移动端阅览
LI Zhe, WU Jun, WANG Shunsen, et al. Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm[J]. 2023, 57(6): 172-180.
DOI：

LI Zhe, WU Jun, WANG Shunsen, et al. Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm[J]. 2023, 57(6): 172-180. DOI： 10.7652/xjtuxb202306019.

摘要

针对船舶管路设计中的路径寻优问题

提出了一种采用A星-遗传算法的船舶管路智能布置方法。首先

建立了船舶管路布置空间模型

包括网格单元模型、管路简化模型、设备障碍物模型和约束规则模型。其次

对传统遗传算法进行了优化设计

在种群初始化阶段

加入障碍物判定函数替换以往其他研究采用的罚函数; 在交叉和变异过程

引入A星算法生成子路径; 引入父子比较环节

每经过交叉、变异一次

便比较一次父代与子代的适应度值; 在选择操作中

对传统的轮盘赌方法进行改进

引进个体的相似度比例

个体的被选择概率由相似度比例和适应度值共同决定。最后

对所提优化A星-遗传算法和粒子群、A星、迷宫-遗传算法进行了仿真对比实验。结果表明:A星-遗传算法在管路的长度、拐角数、能量值、适应度值、最优解次数和平均收敛代数等6项指标上均得到了最优值; 与同为混合算法的迷宫-遗传算法相比

优化A星-遗传算法在两个案例中的最优解次数分别增加了44.4%、100%

平均求解时间分别减少了57.6%、58.1%

平均收敛代数分别减少了36.9%、44.1%。A星-遗传算法在保证管路布置质量的同时

有效提高了寻优效率

其对于船舶管路智能布置的适配性和优越性得到了验证

对提高船舶产业生产力具有一定的意义。

Abstract

For route optimization in ship pipeline design

an intelligent arrangement method of ship pipeline based on Astar-genetic algorithm was proposed. Firstly

the spatial model of ship pipeline arrangement was established

including grid element model

pipeline simplification model

equipment obstacle model and constraint rule model. Next

the traditional genetic algorithm was optimized. In the phase of population initialization

obstacle determination function was added to replace the penalty function used in other studies. In the crossover and mutation process

Astar algorithm was introduced to generate subpaths. The father-child comparison was introduced

and the fitness values of the parents and children were compared every time after crossover and variation. In the selection operation

the traditional roulette method was improved

and the similarity ratio was introduced. The selection probability of an individual was determined by similarity ratio and fitness value. Finally

the optimized Astar-genetic algorithm proposed in this paper

particle swarm

Astar and maze-genetic algorithm were simulated and compared. The results showed that the Astar-genetic algorithm obtained the optimal values in six indexes

i.e.

the length of pipeline

the number of corners

the energy value

the fitness value

the number of optimal solutions and the average convergence algebra. Compared with the maze-genetic algorithm which is also a hybrid algorithm

the number of optimal solutions of the optimized Astar-genetic algorithm in the two cases increased by 44.4% and 100%

the average solving time decreased by 57.6% and 58.1%

and the average convergence algebra decreased by 36.9% and 44.1%

respectively. Astar-genetic algorithm can not only ensure the quality of pipeline arrangement

but also effectively improve the optimization efficiency. Its suitability and superiority for intelligent arrangement of ship pipeline have been verified

and it is of significance for improving the productivity of the ship industry.

关键词

Keywords

references

PARK J H, STORCH R L. Pipe-routing algorithm development: case study of a ship engine room design [J]. Expert Systems with Applications, 2002, 23(3): 299-309.[2] ASMARA A, NIENHUIS U. Automatic piping system in ship [C]//The 5th International Conference on Computer and IT Applications in the Maritime Industries. Sieca Repro, TUD: Delft, 2006: 269-280.[3] 崔康, 雷洪涛, 黄奇奇, 等. 船舶管路辅助布置系统设计与开发 [J]. 机械设计与研究, 2021, 37(5): 186-189, 196.CUI Kang, LEI Hongtao, HUANG Qiqi, et al. Design and development of marine pipeline auxiliary layout system [J]. Machine Design & Research, 2021, 37(5): 186-189, 196.[4] 熊勇, 张加, 余嘉俊, 等. 船舶三维管路智能布局优化算法 [J]. 计算机应用, 2020, 40(7): 2164-2170.XIONG Yong, ZHANG Jia, YU Jiajun, et al. Intelligent layout optimization algorithm for 3D pipelines of ships [J]. Journal of Computer Applications, 2020, 40(7): 2164-2170.[5] LU Huibiao, YU Zhefu, SUN Peiting. Hanging bridge algorithm for Pipe-Routing design in ship engine room [C]//2008 International Conference on Computer Science and Software Engineering. Piscataway, NJ, USA: IEEE, 2008: 153-155.[6] BIAN Xuanyi, LIN Yan, DONG Zongran. Auto-routing methods for complex ship pipe route design [J]. Journal of Ship Production and Design, 2022, 38(2): 100-114.[7] BURDORF A, KAMPCZYK B, LEDERHOSE M, et al. CAPD—computer-aided plant design [J]. Computers & Chemical Engineering, 2004, 28(1/2): 73-81.[8] KANIAT A. Optimization of three-dimensional pipe routing [J]. Ship Technology Research, 2000, 47(3): 111-114.[9] DONG Zongran, LIN Yan. A particle swarm optimization based approach for ship pipe route design [J]. International Shipbuilding Progress, 2017, 63(1/2): 59-84.[10] JIANG Wenying, LIN Yan, CHEN Ming, et al. A co-evolutionary improved multi-ant colony optimization for ship multiple and branch pipe route design [J]. Ocean Engineering, 2015, 102: 63-70.[11] LIN Yan, BIAN Xuanyi, DONG Zongran. A discrete hybrid algorithm based on differential evolution and cuckoo search for optimizing the layout of ship pipe route [J]. Ocean Engineering, 2022, 261: 112164.[12] WANG Yunlong, YU Yanyun, LI Kai, et al. A human-computer cooperation improved ant colony optimization for ship pipe route design [J]. Ocean Engineering, 2018, 150: 12-20.[13] NIU Wentie, SUI Haiteng, NIU Yaxiao, et al. Ship pipe routing design using NSGA-II and coevolutionary algorithm [J]. Mathematical Problems in Engineering, 2016, 2016: 7912863.[14] SUI Haiteng, NIU Wentie. Branch-pipe-routing approach for ships using improved genetic algorithm [J]. Frontiers of Mechanical Engineering, 2016, 11(3): 316-323.[15] 程传奇, 郝向阳, 李建胜, 等. 融合改进A*算法和动态窗口法的全局动态路径规划 [J]. 西安交通大学学报, 2017, 51(11): 137-143.CHENG Chuanqi, HAO Xiangyang, LI Jiansheng, et al. Global dynamic path planning based on fusion of improved A* algorithm and dynamic window approach [J]. Journal of Xi'an Jiaotong University, 2017, 51(11): 137-143.[16] 黄鹤, 李潇磊, 杨澜, 等. 引入改进蝠鲼觅食优化算法的水下无人航行器三维路径规划 [J]. 西安交通大学学报, 2022, 56(7): 9-18.HUANG He, LI Xiaolei, YANG Lan, et al. Three dimensional path planning of unmanned underwater vehicle based on improved manta ray foraging optimization algorithm [J]. Journal of Xi'an Jiaotong University, 2022, 56(7): 9-18.[17] KIMURA H. Automatic designing system for piping and instruments arrangement including branches of pipes [J]. International Conference on Computer Applications in Shipbuilding, 2011,14(8): 93-99.[18] DONG Zongran, LIN Yan. Ship pipe routing method based on genetic algorithm and cooperative coevolution [J]. Journal of Ship Production and Design, 2017, 33(2): 122-134.[19] CLAUSSMANN L, REVILLOUD M, GRUYER D, et al. A review of motion planning for highway autonomous driving [J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(5): 1826-1848.[20] HUANG Xianqun, CAO Qixin, ZHU Xiaoxiao. Mixed path planning for multi-robots in structured hospital environment [J]. The Journal of Engineering, 2019, 2019(14): 512-516.[21] GUO Siyu, ZHANG Xiuguo, DU Yiquan, et al. Path planning of coastal ships based on optimized DQN reward function [J]. Journal of Marine Science and Engineering, 2021, 9(2): 210.[22] AGGARWAL S, KUMAR N. Path planning techniques for unmanned aerial vehicles: a review, solutions, and challenges [J]. Computer Communications, 2020, 149: 270-299.[23] 王运龙, 王晨, 韩洋, 等. 船舶管路智能布局优化设计 [J]. 上海交通大学学报, 2015, 49(4): 513-518.WANG Yunlong, WANG Chen, HAN Yang, et al. Intelligent layout optimization design of ship pipe [J]. Journal of Shanghai Jiaotong University, 2015, 49(4): 513-518.[24] 范小宁, 林焰, 纪卓尚. 船舶管路三维布局优化的变长度编码遗传算法 [J]. 中国造船, 2007, 48(1): 82-90.FAN Xiaoning, LIN Yan, JI Zhuoshang. A variable length coding genetic algorithm to ship pipe path routing optimization in 3D space [J]. Ship Building of China, 2007, 48(1): 82-90.[25] 李喆, 王顺森, 李勇, 等. 船舶管线智能布置的优化Astar算法 [J]. 西安交通大学学报, 2022, 56(12): 108-117.LI Zhe, WANG Shunsen, LI Yong, et al. Optimized Astar algorithm for ship pipeline intelligent layout [J]. Journal of Xi'an Jiaotong University, 2022, 56(12): 108-117.

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