采用A星-遗传算法的船舶管路智能布置

李喆; 吴君; 王顺森; 李勇; 颜晓江; 程上方; 刘观伟

doi:10.7652/xjtuxb202306019

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采用A星-遗传算法的船舶管路智能布置

—————————————————————— | 更新时间：2025-05-08

- 采用A星-遗传算法的船舶管路智能布置
- Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm
- 西安交通大学学报 2023年57卷第6期页码：172-180
- 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：
  
  : 2022-11-16。作者简介: 李喆(1998—),男,硕士生
  王顺森(通信作者),男,副教授,博士生导师。基金项目: 热能动力技术国家重点实验室开放基金资助项目(TPL2020B03)。
- 基金信息：
  
  : 2022-11-16。作者简介: 李喆(1998—),男,硕士生;王顺森(通信作者),男,副教授,博士生导师。基金项目: 热能动力技术国家重点实验室开放基金资助项目(TPL2020B03)。
- DOI：10.7652/xjtuxb202306019
  中图分类号： TP39;TP181
- 网络首发：2023-06-10，
  
  纸质出版：2023
- 稿件说明：
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李喆, 吴君, 王顺森, 等. 采用A星-遗传算法的船舶管路智能布置[J]. 西安交通大学学报, 2023,57(6):172-180.

LI Zhe, WU Jun, WANG Shunsen, et al. Intelligent Arrangement of Ship Pipeline Based on Astar-Genetic Algorithm[J]. 2023, 57(6): 172-180.
李喆, 吴君, 王顺森, 等. 采用A星-遗传算法的船舶管路智能布置[J]. 西安交通大学学报, 2023,57(6):172-180. DOI： 10.7652/xjtuxb202306019.

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

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