Multi‐Objective Optimization and Experimental Research of Ship Form Based on Improved Bare‐Bones Multi‐Objective Particle Swarm Optimization Algorithm

ABSTRACT Ship form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In or...

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Bibliographic Details
Published in:International journal for numerical methods in fluids Vol. 97; no. 3; pp. 267 - 282
Main Authors: Liu, Jie, Zhang, Baoji, Lai, Yuyang, Fang, Liqiao
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2025
Wiley Subscription Services, Inc
Subjects:
algorithm improvement
Algorithms
Bones
Deformation
Deformation effects
Design
Design optimization
Design standards
Experimental research
Free form
Laboratory experimentation
motion response
multi‐objective optimization
Naval engineering
Optimization
Particle swarm optimization
Ship design
Ship hulls
ship model test
Ship models
Shipping
total resistance in waves
ISSN:0271-2091, 1097-0363
Online Access:Get full text
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Summary:ABSTRACT Ship form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In order to effectively improve the diversity and convergence performance of the algorithm, this paper improves the bare‐bones multi‐objective particle swarm optimization (BBMOPSO) algorithm by dynamically adjusting the local and global search step sizes, and verifies the algorithm's reliability through standard function testing. Then, a multi‐objective optimization design framework with high efficiency and high integration is constructed. Taking DTMB 5512 as the research case, Free Form Deformation (FFD) method is used for hull deformation, and the proposed algorithm is used for multi‐objective optimization of resistance performance and motion response. Ship model tests were conducted on the DTMB 5512's original hull. And the numerical simulations were compared with the ship model tests. Finally, under the constructed multi‐objective optimization design framework, satisfactory solutions were obtained through the improved algorithm, which confirms the effectiveness and practicality of the improved algorithm. The results show that the algorithm improved in this paper can provide some theoretical basis and technical support for green ship design and low‐carbon shipping. Conduct ship model tests to verify the reliability of simulation calculations. Introduce search range coefficient and search step size to improve algorithm performance. Build an efficient and highly integrated multi‐objective optimization design framework, and conduct multi‐objective optimization research on resistance performance and motion response.
Bibliography:Funding
This work was supported by the National Natural Science Foundation of China (Nos. 51779135 and 51009087), and the Natural Science Foundation of Shanghai Municipality (No. 14ZR1419500).
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ISSN:0271-2091
1097-0363
DOI:10.1002/fld.5346