Optimal Design of Ship Branch Pipe Route by a Cooperative Co-Evolutionary Improved Particle Swarm Genetic Algorithm

被引:6
作者
Wang, Yunlong [1 ]
Wei, Hao [2 ]
Zhang, Xin [2 ]
Li, Kai [2 ]
Guan, Guan [2 ]
Jin, Chaoguan [2 ]
Yan, Lin [1 ]
机构
[1] Dalian Univ Technol, Sch Naval Architecture, State Key Lab Struct Anal Ind Equipment, Dalian, Peoples R China
[2] Dalian Univ Technol, Sch Naval Architecture, Dalian 116024, Peoples R China
基金
中国国家自然科学基金;
关键词
ship branch pipe route; layout optimization; cooperative co-evolutionary algorithm; particle swarm optimization; genetic algorithm; ANT COLONY OPTIMIZATION;
D O I
10.4031/MTSJ.55.5.18
中图分类号
P75 [海洋工程];
学科分类号
0814 ; 081505 ; 0824 ; 082401 ;
摘要
This paper proposes a cooperative co-evolutionary improved particle swarm genetic (CCIPSG) algorithm for ship branch pipe route design (SBPRD) based on the strategy of first decomposition and then reconstruction. SBPRD is a common type of ship pipe route design connecting one start point and several end points with various performance constraints in 3-D space. The traditional optimization method of SBPRD needs to select the laying sequence of branch pipelines, determine the branch points, and finally conduct the pipeline layout, which is full of uncertainty. The CCIPSG algorithm proposed in this paper aims to avoid the uncertainty of laying sequence and branch points by using the strategy of decomposition before reconstruction. The branch pipe route is deemed as a system; through the process of decomposing, the branch pipe route is decomposed as several single pipe routes with a common start point and different end points. After obtaining the optimal solutions of each single pipe route by using the improved particle swarm genetic algorithm, the co-evolutionary mechanism and overlapped potential energy value method are used to reconstruct the branch pipeline with the minimum total path length and elbows. Compared with the conventional method, the CCIPSG algorithm could not only automatically determinate the laying sequence and branch points but also improve the convergence speed and the quality of the solution. Finally, the simulation result demonstrates the feasibility and efficiency of the proposed method.
引用
收藏
页码:116 / 128
页数:13
相关论文
共 37 条
[1]  
Ando Y., 2011, INT C COMPUTER APPL, P153
[2]  
[Anonymous], 1969, P 6 ANN DES AUT C, DOI DOI 10.1145/800260.809014
[3]  
[Anonymous], 2006, The 5th International Conference on Computer and IT Applications in the Maritime Industries
[4]   Optimal design of submarine pipeline routes by genetic algorithm with different constraint handling techniques [J].
de Lucena, Rodrigo Ribeiro ;
Baioco, Juliana Souza ;
Leite Pires de Lima, Beatriz Souza ;
Albrecht, Carl Horst ;
Jacob, Breno Pinheiro .
ADVANCES IN ENGINEERING SOFTWARE, 2014, 76 :110-124
[5]   Ship Pipe Route Design Using Improved A* Algorithm and Genetic Algorithm [J].
Dong, Zongran ;
Bian, Xuanyi .
IEEE ACCESS, 2020, 8 (08) :153273-153296
[6]   Ship Pipe Routing Method Based on Genetic Algorithm and Cooperative Coevolution [J].
Dong, Zongran ;
Lin, Yan .
JOURNAL OF SHIP PRODUCTION AND DESIGN, 2017, 33 (02) :122-134
[7]  
Fan Xiao-ning, 2009, Journal of Shanghai Jiaotong University, V43, P193
[8]  
Fan XN, 2006, WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, P3103
[9]  
[范小宁 FAN Xiaoning], 2007, [中国造船, Shipbuilding of China], V48, P82
[10]  
Feng Hai-bo, 2010, Journal of System Simulation, V22, P60