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Frontiers of Mechanical Engineering

ISSN 2095-0233

ISSN 2095-0241(Online)

CN 11-5984/TH

邮发代号 80-975

2019 Impact Factor: 2.448

Frontiers of Mechanical Engineering  2016, Vol. 11 Issue (3): 316-323   https://doi.org/10.1007/s11465-016-0384-z
  本期目录
Branch-pipe-routing approach for ships using improved genetic algorithm
Haiteng SUI,Wentie NIU()
Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300072, China
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Abstract

Branch-pipe routing plays fundamental and critical roles in ship-pipe design. The branch-pipe-routing problem is a complex combinatorial optimization problem and is thus difficult to solve when depending only on human experts. A modified genetic-algorithm-based approach is proposed in this paper to solve this problem. The simplified layout space is first divided into three-dimensional (3D) grids to build its mathematical model. Branch pipes in layout space are regarded as a combination of several two-point pipes, and the pipe route between two connection points is generated using an improved maze algorithm. The coding of branch pipes is then defined, and the genetic operators are devised, especially the complete crossover strategy that greatly accelerates the convergence speed. Finally, simulation tests demonstrate the performance of proposed method.

Key wordsbranch pipe    ship industry    piping system    optimization algorithm
收稿日期: 2015-12-23      出版日期: 2016-08-31
Corresponding Author(s): Wentie NIU   
 引用本文:   
. [J]. Frontiers of Mechanical Engineering, 2016, 11(3): 316-323.
Haiteng SUI,Wentie NIU. Branch-pipe-routing approach for ships using improved genetic algorithm. Front. Mech. Eng., 2016, 11(3): 316-323.
 链接本文:  
https://academic.hep.com.cn/fme/CN/10.1007/s11465-016-0384-z
https://academic.hep.com.cn/fme/CN/Y2016/V11/I3/316
Fig.1  
Fig.2  
Fig.3  
Fig.4  
Fig.5  
Fig.6  
Fig.7  
ID Coordinate value
1 (5,1,5)–(15,51,15)
2 (1,27,29)–(30,42,44)
3 (1,1,35)–(21,20,50)
4 (30,5,1)–(45,40,20)
5 (32,1,25)–(47,20,40)
6 (32,1,40)–(47,8,50)
Tab.1  
Parameter Value
Population size 30
Number of generation 100
Mutation probability 0.05
c1 0.85
c2 0.15
Tab.2  
Fig.8  
Fig.9  
Fig.10  
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