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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| Vydáno v: | International journal for numerical methods in fluids Ročník 97; číslo 3; s. 267 - 282 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken, USA
John Wiley & Sons, Inc
01.03.2025
Wiley Subscription Services, Inc |
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| ISSN: | 0271-2091, 1097-0363 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Liu, Jie Zhang, Baoji Fang, Liqiao Lai, Yuyang |
| Author_xml | – sequence: 1 givenname: Jie orcidid: 0000-0001-9999-7274 surname: Liu fullname: Liu, Jie organization: Shanghai Maritime University – sequence: 2 givenname: Baoji surname: Zhang fullname: Zhang, Baoji email: zbj1979@163.com organization: Shanghai Maritime University – sequence: 3 givenname: Yuyang surname: Lai fullname: Lai, Yuyang organization: Beijing Soyotec Co. Ltd – sequence: 4 givenname: Liqiao surname: Fang fullname: Fang, Liqiao organization: Beijing Soyotec Co. Ltd |
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| Cites_doi | 10.1016/j.ocecoaman.2023.106665 10.4031/MTSJ.53.1.10 10.1016/j.ins.2011.06.004 10.1016/j.ins.2016.08.043 10.1016/j.asoc.2022.108532 10.1016/j.advengsoft.2018.05.011 10.13195/j.kzyjc.2014.1378 10.1109/CEC.2012.6256467 10.16208/j.issn1000‐7024.2020.04.011 10.1016/j.pnucene.2023.104740 10.13195/j.kzyjc.2020.1440 10.1108/EC‐05‐2019‐0194 10.1115/1.1412235 10.1016/j.oceaneng.2018.10.025 10.1162/106365600568202 10.1155/2016/6761545 10.1016/j.asoc.2016.09.026 10.1109/4235.996017 10.1016/j.oceaneng.2022.112454 10.1007/s10661‐022‐09909‐6 10.1109/TCYB.2017.2756874 10.1007/s11047‐019‐09729‐7 10.1177/16878132221086689 10.1016/j.oceaneng.2023.114772 10.1007/s003480100293 10.1109/ACCESS.2020.3007846 10.1016/j.eswa.2011.01.169 10.1049/iet‐rsn.2017.0351 10.3233/JIFS‐181005 10.1016/j.csite.2022.102644 10.1002/fld.5291 10.1109/CEC.2002.1007032 10.1016/j.ress.2014.03.006 10.1109/SIS.2008.4668301 10.1504/IJICA.2009.031779 10.1007/s00773‐019‐00640‐5 10.1109/SIS.2003.1202251 |
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Ship form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of... Ship form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship... |
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| SubjectTerms | 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 |
| Title | Multi‐Objective Optimization and Experimental Research of Ship Form Based on Improved Bare‐Bones Multi‐Objective Particle Swarm Optimization Algorithm |
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