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

Optimization of unmanned ship's parametric subdivision based on improved multi-objective PSO
Optimization of unmanned ship's parametric subdivision based on improved multi-objective PSO
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 multi-objective 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.
unmanned ship; cabin compartment division; parametric modeling; multi-objective particle swarm optimization
0029-8018
1-12
Su, Shaojuan
8d90b64e-8e08-49de-beae-f9d76ccdd4c3
Han, Jing
0e18bcab-5434-4606-b635-70fb07250322
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49
Su, Shaojuan
8d90b64e-8e08-49de-beae-f9d76ccdd4c3
Han, Jing
0e18bcab-5434-4606-b635-70fb07250322
Xiong, Yeping
51be8714-186e-4d2f-8e03-f44c428a4a49

Su, Shaojuan, Han, Jing and Xiong, Yeping (2019) Optimization of unmanned ship's parametric subdivision based on improved multi-objective PSO. Ocean Engineering, 194, 1-12, [106617]. (doi:10.1016/j.oceaneng.2019.106617).

Record type: Article

Abstract

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 multi-objective 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.

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Final accepted manuscript Su & Xiong 19 - Accepted Manuscript
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Accepted/In Press date: 19 October 2019
e-pub ahead of print date: 22 November 2019
Published date: 15 December 2019
Keywords: unmanned ship; cabin compartment division; parametric modeling; multi-objective particle swarm optimization

Identifiers

Local EPrints ID: 435069
URI: http://eprints.soton.ac.uk/id/eprint/435069
ISSN: 0029-8018
PURE UUID: f368acd1-5d18-40cb-b0d7-d5371bd5e3ce
ORCID for Yeping Xiong: ORCID iD orcid.org/0000-0002-0135-8464

Catalogue record

Date deposited: 22 Oct 2019 16:30
Last modified: 19 Oct 2020 04:01

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