Citation: | YANG Y Q, YANG M, WANG H, et al. Fast fuzzy reasoning and optimization using a stacked-like-based ship conceptual scheme[J]. Chinese Journal of Ship Research, 2025, 20(X): 1–11 (in Chinese). DOI: 10.19693/j.issn.1673-3185.04500 |
The design of conceptual schemes for ship types often faces challenges in managing the complex coupling relationships among various design parameters. Traditionally, conceptual schemes for different ship types and corresponding requirements rely heavily on the domain knowledge and engineering experience of ship experts. This approach leads to long design cycles and high costs. Therefore, this study proposes a fuzzy reasoning strategy based on a stacked-like structure, aimed at rapidly generating initial solutions for ship conceptual scheme.
This strategy employs fuzzy reasoning based on the user's requirements to derive the performance parameter values of the ship type that meet the user's requirements. These values are then matched with the design scheme library of the master ship to obtain the conceptual scheme of the ship type that meets the user's requirements. Drawing upon the principle of stacked generalization, the method introduces a stack-like structure to systematically combine the initially inferred valid information with the input user requirements in a hierarchical manner. This approach reduces reasoning bias by applying multi-stage verification. Finally, a clustering algorithm is designed to optimize the fusion of the performance parameter library and the master ship design scheme library. This optimization enhances the rationality of the ship conceptual scheme by establishing quantitative relationships between new designs and proven reference solutions.
The numerical examples demonstrate that both the performance parameters of the ship type derived through reasoning and the corresponding conceptual schemes effectively address the real needs of users.
To address the significant inference bias in performance parameters observed in existing methods, this study proposes a rapid fuzzy inference system based on a stack-like structure for ship conceptual design. This system enhances computational efficiency while effectively reducing reasoning deviations during the inference process. By leveraging hierarchical reasoning layers, it improves decision-making accuracy in the early stages of ship design. Existing methods for ship conceptual schemes design often result in unreasonable coupling relationships among the solutions generated through reasoning technology. This study thoroughly explores the commonalities between the fuzzy reasoning results and the parent ship, ensuring that the user's requirements are met while eliminating the imbalance in the design parameter coupling of the ship conceptual schemes. As a result, the efficiency and rationality of generating ship conceptual scheme solutions are significantly improved.
[1] |
由蓬勃, 邹少军. 智能船舶设计与自动化控制研究[J]. 船舶物资与市场, 2024, 32(9): 18–20. doi: 10.19727/j.cnki.cbwzysc.2024.09.006
YOU P B, ZHOU S J. Research on intelligent ship design and automatic control[J]. Marine Equipment/Materials & Marketing, 2024, 32(9): 18–20. doi: 10.19727/j.cnki.cbwzysc.2024.09.006
|
[2] |
周燹. 海上核应急救援无人方舱概念设计研究[D]. 大连: 大连理工大学, 2023.
ZHOU X. Conceptual design of unmanned shelters for nuclear emergency rescue at sea[D]. Dalian: Dalian University of Technology, 2023 (in Chinese).
|
[3] |
李文龙, 谭家华. 我国战略石油储备船概念设计研究[J]. 海洋工程, 2003, 21(1): 73–77. doi: 10.3969/j.issn.1005-9865.2003.01.012
LI W L, TAN J H. A study on the conceptual design of oil storage vessels for national strategic petroleum reserve[J]. The Ocean Engineering, 2003, 21(1): 73–77 (in Chinese). doi: 10.3969/j.issn.1005-9865.2003.01.012
|
[4] |
李晓文, 李平, 林壮, 等. 单体双气垫登陆艇概念设计研究[J]. 船舶工程, 2014, 36(3): 16–19,40. doi: 10.13788/j.cnki.cbgc.2014.0067
LI X W, LI P, LIN Z, et al. Research on conceptual design for landing craft of twin air cushion monohull[J]. Ship Engineering, 2014, 36(3): 16–19,40 (in Chinese). doi: 10.13788/j.cnki.cbgc.2014.0067
|
[5] |
谢云平, 金晨露, 胡冬芳, 等. 三体两栖潮间带风电运维船的概念设计[J]. 船舶工程, 2017, 39(10): 1–4,31. doi: 10.13788/j.cnki.cbgc.2017.10.001
XIE Y P, JIN C L, HU D F, et al. Concept design of amphibious maintenance trimaran for offshore intertidal wind farms[J]. Ship Engineering, 2017, 39(10): 1–4,31 (in Chinese). doi: 10.13788/j.cnki.cbgc.2017.10.001
|
[6] |
李学斌, 甘霖. 舰船概念设计多目标优化和多属性决策研究[J]. 中国舰船研究, 2008, 3(3): 14–17,33. doi: 10.3969/j.issn.1673-3185.2008.03.003
LI X B, GAN L. Multi-objective optimization and multi-attribute decision making study of naval ship’s conceptual design[J]. Chinese Journal of Ship Research, 2008, 3(3): 14–17,33 (in Chinese). doi: 10.3969/j.issn.1673-3185.2008.03.003
|
[7] |
王健, 谢伟, 熊治国, 等. 基于多目标遗传算法的飞行甲板参数化设计优化方法[J]. 中国舰船研究, 2013, 8(1): 7–12. doi: 10.3969/j.issn.1673-3185.2013.01.002
WANG J, XIE W, XIONG Z G, et al. Parametric optimization of the flight deck design based on the multi-objective genetic algorithm[J]. Chinese Journal of Ship Research, 2013, 8(1): 7–12 (in Chinese) doi: 10.3969/j.issn.1673-3185.2013.01.002
|
[8] |
王健, 谢伟, 王涛, 等. 基于目标分流方法的船舶概念方案多学科设计优化[J]. 中国舰船研究, 2017, 12(5): 22–29. doi: 10.3969/j.issn.1673-3185.2017.05.003
WANG J, XIE W, WANG T, et al. Application of analytical target cascading method in multidisciplinary design optimization of ship conceptual design[J]. Chinese Journal of Ship Research, 2017, 12(5): 22–29 (in Chinese) doi: 10.3969/j.issn.1673-3185.2017.05.003
|
[9] |
任伟, 熊鹰, 齐万江. 船舶主尺度及船型系数方案设计生成系统研究[C]//第十六届中国国际船艇展暨高性能船学术报告会论文集. 上海: 中国造船工程学会, 2011: 221−226.
REN W, XIONG Y, QI W J. Research on the design generation system for ship principal dimensions and hull form coefficients[C]//Proceedings of the 16th China International Boat Show & High-Performance Vessel Academic Conference. Shanghai: The Chinese Society of Naval Architects and Marine Engineers, 2011: 221−226 (in Chinese).
|
[10] |
周奇, 陈立, 许辉, 等. 基于组合赋权TOPSIS法和粒子群的船舶概念优化设计[J]. 舰船科学技术, 2014, 36(1): 62–69. doi: 10.3404/j.issn.1672-7649.2014.01.013
ZHOU Q, CHEN L, XU H, et al. Application of combination weight TOPSIS method and multi-objective particle swarm optimization in conceptual ship design[J]. Ship Science and Technology, 2014, 36(1): 62–69 (in Chinese). doi: 10.3404/j.issn.1672-7649.2014.01.013
|
[11] |
刘传云, 马运义, 刘明静, 等. 多种群变异遗传算法在舰船概念方案设计中的应用[C]//国家科学基金海洋工程“十二五”期间发展战略高层研讨会暨2009年全国船舶与海洋工程学科博士生学术会议论文集. 武汉: 教育部学位管理与研究生教育司, 2009: 13−15.
LIUC Y, MA Y Y, LIU M J, et al. Application of the multiple population mutation genetic algorithm in the conceptual design of warship[C]//Proceedings of the National Science Foundation Ocean Engineering "12th Five-Year Plan" Development Strategy High-Level Seminar & 2009 National Doctoral Academic Conference on Naval Architecture and Ocean Engineering. Wuhan, 2009: 13−15 (in Chinese).
|
[12] |
MAURO F, SALEM A. Development of regression models for estimating main particulars of RoPax vessels in the conceptual design stage[J]. Ocean Engineering, 2025, 333: 121407. doi: 10.1016/j.oceaneng.2025.121407
|
[13] |
GRAY A W, SINGER D J. A hybrid agent type-1 fuzzy logic system for set-based conceptual ship design communications and negotiations[J]. Naval Engineers Journal, 2016, 128(1): 77–89.
|
[14] |
DEGAN G, BRAIDOTTI L, MARINÒ A, et al. LCTC ships concept design in the North Europe-Mediterranean transport scenario focusing on intact stability issues[J]. Journal of Marine Science and Engineering, 2021, 9(3): 278. doi: 10.3390/jmse9030278
|
[15] |
HIRAYAMA S, ICHINOSE Y, WANAKA S, et al. Dynamic capabilities of maritime infrastructure: conceptual design of merchant vessels with usability in crisis[J]. Journal of Marine Science and Technology, 2023, 28(2): 422–438. doi: 10.1007/s00773-023-00932-x
|
[16] |
ALVARADO D R, PATERNINA L A, PAIPA E G. Synthesis model for the conceptual design of inland cargo vessels to operate on the Magdalena River[J]. Brodogradnja, 2022, 73(4): 13–37. doi: 10.21278/brod73402
|
[17] |
TADROS M, VENTURA M, SOARES C G. A nonlinear optimization tool to simulate a marine propulsion system for ship conceptual design[J]. Ocean Engineering, 2020, 210: 107417. doi: 10.1016/j.oceaneng.2020.107417
|
[18] |
杨萌, 龚俊斌, 曹晋, 等. 基于智能模糊推理系统的船型概念方案快速生成研究[J]. 中国舰船研究, 2024, 19(6): 45–55. doi: 10.19693/j.issn.1673-3185.03718
YANG M, GONG J B, CAO J, et al. Rapid ship hull conceptual scheme design based on intelligent fuzzy inference system[J]. Chinese Journal of Ship Research, 2024, 19(6): 45–55 (in both Chinese and English). doi: 10.19693/j.issn.1673-3185.03718
|
[19] |
ZADEH L A. Fuzzy sets[J]. Information and Control, 1965, 8(3): 338–353. doi: 10.1016/S0019-9958(65)90241-X
|
[20] |
ZHOU T, CHUNG F L, WANG S T. Deep TSK fuzzy classifier with stacked generalization and triplely concise interpretability guarantee for large data[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(5): 1207–1221. doi: 10.1109/TFUZZ.2016.2604003
|
[21] |
ZHOU T, ZHOU Y, GAO S. Quantitative-integration-based TSK fuzzy classification through improving the consistency of multi-hierarchical structure[J]. Applied Soft Computing, 2021, 106: 107350. doi: 10.1016/j.asoc.2021.107350
|
[22] |
刘丙杰, 胡昌华. 基于高斯隶属函数的模糊定性仿真[J]. 系统工程与电子技术, 2006, 28(7): 1098–1102. doi: 10.3321/j.issn:1001-506X.2006.07.041
LIU B J, HU C H. Fuzzy qualitative simulation with Gauss membership function[J]. Systems Engineering and Electronics, 2006, 28(7): 1098–1102 (in Chinese). doi: 10.3321/j.issn:1001-506X.2006.07.041
|
[23] |
OZKAN I, TURKSEN I B. Upper and lower values for the level of fuzziness in FCM[J]. Information Sciences, 2007, 177(23): 5143–5152. doi: 10.1016/j.ins.2007.06.028
|
[24] |
WOLPERT D H. Stacked generalization[J]. Neural Networks, 1992, 5(2): 241–259. doi: 10.1016/S0893-6080(05)80023-1
|
[25] |
IKOTUN A M, EZUGWU A E, ABUALIGAH L, et al. K-means clustering algorithms: a comprehensive review, variants analysis, and advances in the era of big data[J]. Information Sciences, 2023, 622: 178–210. doi: 10.1016/j.ins.2022.11.139
|
[26] |
GOICOVICH I, OLIVARES P, ROMÁN C, et al. Fiber clustering acceleration with a modified Kmeans++ algorithm using data parallelism[J]. Frontiers in Neuroinformatics, 2021, 15: 727859. doi: 10.3389/fninf.2021.727859
|