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Optimization of unmanned ship's parametric subdivision based on improved multi-objective PSO

机译:基于改进多目标PSO的无人舰船参数细分优化

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摘要

The optimization of ship's subdivision arrangement is an important part of ship general layout design. However, optimization to an unmanned ship's subdivision considering the complex multi-objective is less studied. In this paper, the multi-objective optimization of a V-type non-ballasted water unmanned ship compartment division is investigated based on parametric model. The principal rules of cabin division for unmanned ship are determined first. The shape and composition of the longitudinal inner shell layout and transverse inner shell structure are studied by three-dimensional parametric representation method. The cabin capacity, bending moment and water immersion factor are used as the objective functions to establish the mathematical model. The improved multiobjective particle swarm optimization (PSO) algorithm are used to optimize the unmanned ship's subdivision arrangement in which the generated front-end solutions are normalized and sorted by the distance from the origin to solved the multiple objectives. The grey relational degree calculation method is applied to verify the method. Finally, different subdivision scheme based on different objective is given. The findings provide useful guidelines for the design optimization of non-ballast water unmanned ships.
机译:船舶细分布置的优化是船舶总体布局设计的重要组成部分。然而,考虑到复杂的多目标对无人舰船细分的优化研究较少。基于参数模型,研究了V型无压载水无人船舱划分的多目标优化。首先确定无人船舱划分的主要规则。通过三维参数表示方法研究了纵向内壳布局和横向内壳结构的形状和组成。以座舱容量,弯矩和水浸系数为目标函数,建立数学模型。改进的多目标粒子群算法(PSO)用于优化无人舰船的细分方案,其中将生成的前端解归一化并按距原点的距离进行排序,以解决多个目标。应用灰色关联度计算方法对该方法进行了验证。最后,给出了基于不同目标的不同细分方案。研究结果为非压载水无人船的设计优化提供了有用的指导。

著录项

  • 来源
    《Ocean Engineering 》 |2019年第15期| 106617.1-106617.12| 共12页
  • 作者单位

    Dalian Maritime Univ Naval Architecture & Ocean Engn Coll Dalian 116026 Liaoning Peoples R China|Dalian Maritime Univ Unmanned Ships Collaborat Innovat Inst Dalian 116026 Liaoning Peoples R China|Univ Southampton Fac Engn & Phys Sci Boldrewood Innovat Campus Southampton SO16 7QF Hants England;

    Dalian Maritime Univ Naval Architecture & Ocean Engn Coll Dalian 116026 Liaoning Peoples R China;

    Univ Southampton Fac Engn & Phys Sci Boldrewood Innovat Campus Southampton SO16 7QF Hants England;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Unmanned ship; Subdivision; Parametric model; Multi-objective particle swarm optimization; Grey relational degree;

    机译:无人船细分;参数模型多目标粒子群算法;灰色关联度;

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