PG-MACO Optimization Method for Ship Pipeline Layout

doi:10.16183/j.cnki.jsjtu.2022.508

Abstract

Abstract:

Aimed at the problem of low efficiency of ship pipeline design, an optimization method of pipeline layout is proposed. An optimization mathematical model is established by comprehensively considering the engineering background of safety, economy, coordination and operability, and the defects of ant colony optimization algorithm in dealing with mixed pipeline layout conditions are improved. A spatial state transition strategy for optimizing feasible solution search, a pheromone diffusion mechanism for improving pheromone inspiration effect and accelerating algorithm convergence are proposed, and a multi-ant colony co-evolution mechanism is designed for mixed pipeline layout conditions. Based on the secondary development technology, the application of this method in the third-party design software is realized, and verified by a nuclear primary pipeline layout project. The results show that the pheromone Gaussian diffusion multi ant colony optimization (PG-MACO) algorithm has a better performance and layout effect than the traditional ant colony algorithm. The routing efficiency is improved by 58.38%, the convergence algebra is shortened by 43.24%, the pipeline length is shortened by 33.88%, and the number of pipeline bends is reduced by 41.67%, which verifies the effectiveness and engineering practicability of the proposed method.

Key words: ship pipeline, layout optimization, ant colony optimization algorithm, pheromone diffusion

CLC Number:

LIN Yan, JIN Tingyu, YANG Yuchao. PG-MACO Optimization Method for Ship Pipeline Layout[J]. Journal of Shanghai Jiao Tong University, 2024, 58(7): 1027-1035.

Figures/Tables 13

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Tab.1

Tab.2

Tab.3

Fig.6

Fig.7

Tab.4

Fig.8

Tab.5

References 22

[1]	DONG Z R, BIAN X Y, ZHAO S. Ship pipe route design using improved multi-objective ant colony optimization[J]. Ocean Engineering, 2022, 258: 1-14.
[2]	WANG Y L, YU Y Y, LI K, et al. A human-computer cooperation improved ant colony optimization for ship pipe route design[J]. Ocean Engineering, 2018, 150: 12-20.
[3]	JIANG W Y, LIN Y, CHEN M, 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.
[4]	陈杨. 基于改进蚁群算法和SolidWorks二次开发的船舶管路规划研究[D]. 镇江: 江苏科技大学, 2019.
	CHEN Yang. Research on ship pipeline planning based on improved ant colony algorithm and secondary development of SolidWorks[D]. Zhenjiang: Jiangsu University of Science and Technology, 2019.
[5]	范小宁, 林焰, 纪卓尚. 多蚁群协进化的船舶多管路并行布局优化[J]. 上海交通大学学报, 2009, 43(2): 193-197.
	FAN Xiaoning, LIN Yan, JI Zhuoshang. Multi ant colony cooperative coevolution for optimization of ship multi pipe parallel routing[J]. Journal of Shanghai Jiao Tong University, 2009, 43(2): 193-197.
[6]	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(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]. Shipbuilding of China, 2007(1): 82-90.
[7]	董宗然, 楼偶俊, 管官. 基于改进遗传算法的船舶管路布局设计[J]. 计算机工程与应用, 2020, 56(19): 252-260. doi: 10.3778/j.issn.1002-8331.1906-0251
	DONG Zongran, LOU Oujun, GUAN Guan. Ship pipe route design based on improved genetic algorithm[J]. Computer Engineering and Applications, 2020, 56(19): 252-260. doi: 10.3778/j.issn.1002-8331.1906-0251
[8]	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 Jiao Tong University, 2015, 49(4): 513-518.
[9]	DONG Z R, LIN Y. A particle swarm optimization based approach for ship pipe route design[J]. International Shipbuilding Progress, 2017, 63(1-2): 59-84.
[10]	LIN Y, BIAN X Y, DONG Z R. A discrete hybrid algorithm based on Differential Evolution and Cuckoo Search for optimizing the layout of ship pipe route[J]. Ocean Engineering, 2022, 261: 1-13.
[11]	BIAN X, LIN Y, DONG Z. Auto-routing methods for complex ship pipe route design[J]. Journal of Ship Production and Design, 2022, 38(2): 100-114.
[12]	董宗然, 林焰. 基于协同进化和并行计算的船舶管路布置方法[J]. 大连理工大学学报, 2016, 56(4): 367-374.
	DONG Zongran, LIN Yan. Method of ship pipe routing based on co-evolution and parallel computing[J]. Journal of Dalian University of Technology, 2016, 56(4): 367-374.
[13]	邢佳鹏. 基于改进A-star算法的船舶三维管路布局优化设计[D]. 大连: 大连理工大学, 2021.
	XING Jiapeng. Optimal design of ship three-dimensional pipeline layout based on improved A-star algorithm[D]. Dalian: Dalian University of Technology, 2021.
[14]	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. doi: 10.11772/j.issn.1001-9081.2020010075
	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. doi: 10.11772/j.issn.1001-9081.2020010075
[15]	DONG Z, BIAN X. Ship pipe route design using improved A^* algorithm and genetic algorithm[J]. IEEE Access, 2020, 8: 153273-153296.
[16]	董宗然, 王法胜, 楼偶俊, 等. 基于改进NSGA-II 的船舶管路路径设计[J]. 计算机集成制造系统, 2022, 28(4): 1129-1142.
	DONG Zongran, WANG Fasheng, LOU Oujun, et al. Ship pipe route design based on improved NSGA-II[J]. Computer Integrated Manufacturing Systems, 2022, 28(4): 1129-1142.
[17]	WANG Y, WEI H, ZHANG X, et al. Optimal design of ship branch pipe route by a cooperative co-evolutionary improved particle swarm genetic algorithm[J]. Marine Technology Society Journal, 2021, 55(5): 116-128.
[18]	徐浩天, 季伟东, 孙小晴, 等. 基于正态分布衰减惯性权重的粒子群优化算法[J]. 深圳大学学报(理工版), 2020, 37(2): 208-213.
	XU Haotian, JI Weidong, SUN Xiaoqing, et al. A PSO algorithm with inertia weight decay by normal distribution[J]. Journal of Shenzhen University (Science & Engineering), 2020, 37(2): 208-213.
[19]	邹玉堂, 任光, 路慧彪. 船舶管路布置仿真模型简化[J]. 上海海事大学学报, 2010, 31(1): 72-76.
	ZHOU Yutang, REN Guang, LU Huibiao. Simulation model simplification for pipe route design of ship[J]. Journal of Shanghai Maritime University, 2010, 31(1): 72-76.
[20]	林焰, 金庭宇. 一种基于PG-MACO算法的舰船管路布置设计: CN 202211218580.2[P].2022-12-16[2022-12-09].
	LIN Yan, JIN Tingyu. A ship pipeline layout design based on PG-MACO algorithm: CN 202211218580.2[P]. 2022-12-16[2022-12-09].
[21]	罗朝阳. 基于CATIA游艇机舱可视化设计[D]. 武汉: 武汉理工大学, 2013.
	LUO Chaoyang. The yacht cabin visual design based on CATIA[D]. Wuhan: Wuhan University of Technology, 2013.
[22]	大连理工大学. 舰船管道自动布置设计PD-MACO算法软件[M]. 辽宁: 中华人民共和国国家版权局, 2022.
	Dalian University of Technology. PD-MACO algorithm software for automatic layout design of ship pipelines[M]. Liaoning: People’s Republic of China (PRC) National Copyright Administration, 2022.

管路序号	起点接口/mm	终点接口/mm
P1	(6 500, 4 975, 1 700)	(7 220, 5 100, 1 000)
P2	(6 500, 7 170, 1 700)	(7 220, 7 100, 1 000)
P3	(1 000, 520, 4 750)	(3 700, 460, 3 700)
P4	(1 000, 1 700, 5 500)	(930, 1 400, 7 100)
P5	(930, 1 500, 7 400)	(75, 3 240, 4 140)
P6	(1 000, 2 300, 3 200)	(75, 3 240, 3 800)
P7-1	(6 150, 4 975, 2 125)	(930, 1 400, 7 500)
P7-2	(6 150, 4 975, 2 125)	(6 150, 7 170, 2 125)
P8-1	(8 980, 5 720, 3 520)	(7 780, 5 100, 1 000)
P8-2	(8 980, 5 720, 3 520)	(7 780, 7 100, 1 000)

设备序号	极小对角点/mm	极大对角点/mm
Equ_1	(0, 3 162, 3 831)	(150, 3 377, 4 111)
Equ_2	(857, 1 312, 7 138)	(1 007, 1 497, 7 483)
Equ_3	(174, 516, 3 051)	(1 825, 2 273, 5 483)
Equ_4_1	(3 729, 157, 3 241)	(5 270, 758, 4 052)
Equ_4_2	(8 682, 5 751, 3 064)	(9 283, 7 293, 3 875)
Equ_5_1	(6 169, 4 824, 1 725)	(6 650, 5 124, 2 725)
Equ_5_2	(6 169, 7 024, 1 725)	(6 650, 7 324, 2 725)
Equ_6_1	(7 249, 4 999, 874)	(7 750, 5 200, 1 125)
Equ_6_2	(7 249, 6 999, 874)	(7 750, 7 200, 1 125)

参数	取值
种群数量	20
迭代次数	100
信息素总量	500
信息素挥发因子	0.4
信息素启发因子	2
距离启发因子	2
高斯分布期望	0
高斯分布方差基值	3
高斯分布方差下限	1

参数	布置方案比较		降幅/%
参数	传统蚁群	PG-MACO	降幅/%
个体寻路时间/ms	1 110.18	462.05	58.38
收敛代数	37	21	43.24
管路长度/mm	51 650	34 150	33.88
折弯数	36	21	41.67

管路序号	GA-A^*		PG-MACO
管路序号	长度/mm	折弯次数	长度/mm	折弯次数
P1	5 700	3	5 500	4
P2	1 850	3	1 800	3
P3	2 900	3	2 650	3
P4	1 400	3	1 250	3
P5	4 000	3	3 950	3
P6	18 450	6	13 400	7
P7	15 400	7	15 450	9
P8	8 300	4	8 100	4
总计	58 000	32	52 100	36