计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (7): 344-354.DOI: 10.3778/j.issn.1002-8331.2212-0298

• 工程与应用 • 上一篇    

基于改进蚁群优化算法的船舶管路布局设计

董宗然,陈恒,卞璇屹,楼偶俊   

  1. 1.大连外国语大学 软件学院,辽宁 大连 116044
    2.大连外国语大学 图书情报研究所,辽宁 大连 116044
    3.大连理工大学 船舶工程学院,辽宁 大连 116024
  • 出版日期:2024-04-01 发布日期:2024-04-01

Ship Pipe Layout Design Based on Improved Ant Colony Optimization

DONG Zongran, CHEN Heng, BIAN Xuanyi, LOU Oujun   

  1. 1.School of Software, Dalian University of Foreign Languages, Dalian, Liaoning 116044, China
    2.Institute of Library and Information, Dalian University of Foreign Languages, Dalian, Liaoning 116044, China
    3.School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian, Liaoning 116024, China
  • Online:2024-04-01 Published:2024-04-01

摘要: 为了实现在多种约束和目标限制下,能以较短时间为工程师提供多种优质船舶布管方案,提出一种基于改进蚁群优化算法的新型船舶管路布局设计方法。该方法基于网格空间分解模型,将适应度函数中的优化目标进行规范化,对蚁群优化算法的关键步骤,如:蚂蚁行进方向选择、信息素更新机制、蚂蚁寻路过程等进行改进,采用最优解集合保存适应度相同但管路布局效果不同的多个优解,引入辅助点策略和并行计算机制提升算法整体寻优能力和效率。通过仿真算例验证了算法的有效性和先进性。

关键词: 船舶管路布局设计, 路径设计, 蚁群优化, 并行计算

Abstract: In order to provide engineers with a variety of high-quality ship pipe layouts in a relatively short time under multiple constraints and objectives, a novel ship pipe layout design method based on the modified ant colony optimization (MdACO) is proposed. The method works in a grid-decomposition space model, firstly the optimization objectives of the fitness function are normalized, and then the key steps of MdACO are improved, such as the selection of ant-moving direction, the mechanism of pheromone update, and the procedure of ant route searching. The best solution set is used to save lots of optimization individuals with the same fitness but different layout effects. Moreover, the auxiliary connection point and parallel computing are integrated to improve the searching ability and efficiency of the algorithm. Finally, the effectiveness and advancement of the algorithm are verified by the simulation test.

Key words: ship pipe layout design, route design, ant colony optimization, parallel computing