{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,23]],"date-time":"2026-08-23T16:27:07Z","timestamp":1787502427076,"version":"build-2736575974"},"reference-count":137,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62206113"],"award-info":[{"award-number":["62206113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62206113"],"award-info":[{"award-number":["62206113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62206113"],"award-info":[{"award-number":["62206113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62206113"],"award-info":[{"award-number":["62206113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62073155"],"award-info":[{"award-number":["62073155"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20221067, BK20230923"],"award-info":[{"award-number":["BK20221067, BK20230923"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"name":"High-End Foreign Expert Recruitment Plan","award":["G2023144007L"],"award-info":[{"award-number":["G2023144007L"]}]},{"name":"High-End Foreign Expert Recruitment Plan","award":["G2023144007L"],"award-info":[{"award-number":["G2023144007L"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Membr Comput"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s41965-024-00169-6","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T09:02:31Z","timestamp":1724835751000},"page":"318-334","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Research progress on intelligent optimization techniques for energy-efficient design of ship hull forms"],"prefix":"10.1007","volume":"6","author":[{"given":"Shuwei","family":"Zhu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siying","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaifeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Fang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leilei","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"169_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.122288","volume":"239","author":"NL Trivyza","year":"2022","unstructured":"Trivyza, N. L., Rentizelas, A., Theotokatos, G., & Boulougouris, E. (2022). Decision support methods for sustainable ship energy systems: A state-of-the-art review. Energy, 239, 122288.","journal-title":"Energy"},{"key":"169_CR2","doi-asserted-by":"publisher","first-page":"4639","DOI":"10.3390\/app10134639","volume":"10","author":"CA Frangopoulos","year":"2020","unstructured":"Frangopoulos, C. A. (2020). Developments, trends, and challenges in optimization of ship energy systems. Applied Sciences, 10, 4639.","journal-title":"Applied Sciences"},{"key":"169_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2019.111879","volume":"197","author":"E Esmailian","year":"2019","unstructured":"Esmailian, E., Gholami, H., R\u00f8stvik, H. N., & Menhaj, M. B. (2019). A novel method for optimal performance of ships by simultaneous optimisation of hull-propulsion-bipv systems. Energy Conversion and Management, 197, 111879.","journal-title":"Energy Conversion and Management"},{"key":"169_CR4","doi-asserted-by":"publisher","first-page":"835","DOI":"10.3390\/jmse11040835","volume":"11","author":"M Tadros","year":"2023","unstructured":"Tadros, M., Ventura, M., & Guedes Soares, C. (2023). Review of the decision support methods used in optimizing ship hulls towards improving energy efficiency. Journal of Marine Science and Engineering, 11, 835.","journal-title":"Journal of Marine Science and Engineering"},{"key":"169_CR5","first-page":"947","volume":"28","author":"Y Chi","year":"2016","unstructured":"Chi, Y., & Huang, F. (2016). An overview of simulation-based hydrodynamic design of ship hull forms. Journal of Hydrodynamics. Ser. B, 28, 947\u2013960.","journal-title":"Ser. B"},{"key":"169_CR6","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/s00158-017-1775-4","volume":"57","author":"M Diez","year":"2018","unstructured":"Diez, M., Campana, E. F., & Stern, F. (2018). Stochastic optimization methods for ship resistance and operational efficiency via cfd. Structural and Multidisciplinary Optimization, 57, 735\u2013758.","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"169_CR7","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1080\/17445302.2017.1347231","volume":"13","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Tezdogan, T., Zhang, B., Xu, L., & Lai, Y. (2018). Hull form optimisation in waves based on cfd technique. Ships and Offshore Structures, 13, 149\u2013164.","journal-title":"Ships and Offshore Structures"},{"key":"169_CR8","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.oceaneng.2018.11.044","volume":"172","author":"Y Lin","year":"2019","unstructured":"Lin, Y., Yang, Q., & Guan, G. (2019). Automatic design optimization of swath applying cfd and rsm model. Ocean Engineering, 172, 146\u2013154.","journal-title":"Ocean Engineering"},{"key":"169_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2020.102071","volume":"97","author":"A Miao","year":"2020","unstructured":"Miao, A., Zhao, M., & Wan, D. (2020). Cfd-based multi-objective optimisation of s60 catamaran considering demihull shape and separation. Applied Ocean Research, 97, 102071.","journal-title":"Applied Ocean Research"},{"key":"169_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s40430-021-03335-4","volume":"44","author":"A Nazemian","year":"2022","unstructured":"Nazemian, A., & Ghadimi, P. (2022). Multi-objective optimization of ship hull modification based on resistance and wake field improvement: combination of adjoint solver and cad-cfd-based approach. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 44, 1\u201327.","journal-title":"Journal of the Brazilian Society of Mechanical Sciences and Engineering"},{"key":"169_CR11","doi-asserted-by":"crossref","unstructured":"Islam, H., Ventura, M., Soares, C.\u00a0G., Tadros, M. & Abdelwahab, H. (2022). Comparison between empirical and cfd based methods for ship resistance and power prediction. Trends in Maritime Technology and Engineering, 347\u2013357.","DOI":"10.1201\/9781003320272-38"},{"key":"169_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-8423-2","volume-title":"Research on ship design and optimization based on simulation-based design (SBD) Technique","author":"B Zhang","year":"2019","unstructured":"Zhang, B., & Zhang, S. (2019). Research on ship design and optimization based on simulation-based design (SBD) Technique. Springer."},{"key":"169_CR13","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1017\/S0962492919000060","volume":"28","author":"J Larson","year":"2019","unstructured":"Larson, J., Menickelly, M., & Wild, S. M. (2019). Derivative-free optimization methods. Acta Numerica, 28, 287\u2013404.","journal-title":"Acta Numerica"},{"key":"169_CR14","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.apor.2016.04.006","volume":"59","author":"A Serani","year":"2016","unstructured":"Serani, A., et al. (2016). Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms. Applied Ocean Research, 59, 115\u2013128.","journal-title":"Applied Ocean Research"},{"key":"169_CR15","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1109\/TASE.2020.3027532","volume":"19","author":"Z Zhao","year":"2022","unstructured":"Zhao, Z., Liu, S., Zhou, M., You, D., & Guo, X. (2022). Heuristic scheduling of batch production processes based on petri nets and iterated greedy algorithms. IEEE Transactions on Automation Science and Engineering, 19, 251\u2013261.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"169_CR16","doi-asserted-by":"publisher","first-page":"1941","DOI":"10.1109\/TASE.2021.3062994","volume":"19","author":"Z Zhao","year":"2022","unstructured":"Zhao, Z., Zhou, M., & Liu, S. (2022). Iterated greedy algorithms for flow-shop scheduling problems: A tutorial. IEEE Transactions on Automation Science and Engineering, 19, 1941\u20131959.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"169_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.113127","volume":"266","author":"E Esmailian","year":"2022","unstructured":"Esmailian, E., Steen, S., & Koushan, K. (2022). Ship design for real sea states under uncertainty. Ocean Engineering, 266, 113127.","journal-title":"Ocean Engineering"},{"key":"169_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.110684","volume":"248","author":"F Hu","year":"2022","unstructured":"Hu, F., et al. (2022). Conceptual design of a long-range autonomous underwater vehicle based on multidisciplinary optimization framework. Ocean Engineering, 248, 110684.","journal-title":"Ocean Engineering"},{"key":"169_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2019.05.027","volume":"90","author":"M Zhu","year":"2019","unstructured":"Zhu, M., Hahn, A., Wen, Y.-Q., & Sun, W.-Q. (2019). Optimized support vector regression algorithm-based modeling of ship dynamics. Applied Ocean Research, 90, 101842.","journal-title":"Applied Ocean Research"},{"key":"169_CR20","first-page":"36","volume":"16","author":"J Qian","year":"2012","unstructured":"Qian, J., Mao, X., Wang, X., et al. (2012). Ship hull automated optimization of minimum resistance via cfd and rsm technique. Journal of Ship Mechanics, 16, 36\u201343.","journal-title":"Journal of Ship Mechanics"},{"key":"169_CR21","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s00773-015-0337-y","volume":"21","author":"H Kim","year":"2016","unstructured":"Kim, H., Choi, J., & Chun, H. (2016). Hull-form optimization using parametric modification functions and particle swarm optimization. Journal of Marine Science and Technology, 21, 129\u2013144.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR22","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1007\/s12204-017-1882-9","volume":"22","author":"Y Hou","year":"2017","unstructured":"Hou, Y., You, Y., & Liang, X. (2017). Minimum resistance ship hull uncertainty optimization design based on simulation-based design method. Journal of Shanghai Jiaotong University (Science), 22, 657\u2013663.","journal-title":"Journal of Shanghai Jiaotong University (Science)"},{"key":"169_CR23","doi-asserted-by":"crossref","unstructured":"Viana, F.\u00a0A., Gogu, C. & Goel, T. (2021). Surrogate modeling: tricks that endured the test of time and some recent developments. Structural and Multidisciplinary Optimization, 1\u201328.","DOI":"10.1007\/s00158-021-03001-2"},{"key":"169_CR24","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.oceaneng.2018.08.059","volume":"167","author":"T Tezdogan","year":"2018","unstructured":"Tezdogan, T., et al. (2018). An investigation into fishing boat optimisation using a hybrid algorithm. Ocean Engineering, 167, 204\u2013220.","journal-title":"Ocean Engineering"},{"key":"169_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compfluid.2019.104259","volume":"192","author":"P He","year":"2019","unstructured":"He, P., Filip, G., Martins, J. R., & Maki, K. J. (2019). Design optimization for self-propulsion of a bulk carrier hull using a discrete adjoint method. Computers & Fluids, 192, 104259.","journal-title":"Computers & Fluids"},{"key":"169_CR26","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1080\/19942060.2017.1343751","volume":"12","author":"S Zhang","year":"2018","unstructured":"Zhang, S., Zhang, B., Tezdogan, T., Xu, L., & Lai, Y. (2018). Computational fluid dynamics-based hull form optimization using approximation method. Engineering Applications of Computational Fluid Mechanics, 12, 74\u201388.","journal-title":"Engineering Applications of Computational Fluid Mechanics"},{"key":"169_CR27","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.apor.2018.09.003","volume":"80","author":"Z Zong","year":"2018","unstructured":"Zong, Z., Hong, Z., Wang, Y., & Hefazi, H. (2018). Hull form optimization of trimaran using self-blending method. Applied Ocean Research, 80, 240\u2013247.","journal-title":"Applied Ocean Research"},{"key":"169_CR28","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1007\/s00773-020-00721-w","volume":"26","author":"G Guan","year":"2021","unstructured":"Guan, G., Yang, Q., Wang, Y., Zhou, S., & Zhuang, Z. (2021). Parametric design and optimization of swath for reduced resistance based on evolutionary algorithm. Journal of Marine Science and Technology, 26, 54\u201370.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109050","volume":"230","author":"W Luo","year":"2021","unstructured":"Luo, W., Guo, X., Dai, J., & Rao, T. (2021). Hull optimization of an underwater vehicle based on dynamic surrogate model. Ocean Engineering, 230, 109050.","journal-title":"Ocean Engineering"},{"key":"169_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2020.106958","volume":"198","author":"Q Yang","year":"2020","unstructured":"Yang, Q., Lin, Y., & Guan, G. (2020). Improved sequential sampling for meta-modeling promotes design optimization of swath. Ocean Engineering, 198, 106958.","journal-title":"Ocean Engineering"},{"key":"169_CR31","first-page":"173","volume":"28","author":"F Huang","year":"2016","unstructured":"Huang, F., & Chi, Y. (2016). Hull form optimization of a cargo ship for reduced drag. Journal of Hydrodynamics. Ser. B, 28, 173\u2013183.","journal-title":"Ser. B"},{"key":"169_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2019.05.026","volume":"90","author":"A Copped\u00e8","year":"2019","unstructured":"Copped\u00e8, A., Gaggero, S., Vernengo, G., & Villa, D. (2019). Hydrodynamic shape optimization by high fidelity cfd solver and gaussian process based response surface method. Applied Ocean Research, 90, 101841.","journal-title":"Applied Ocean Research"},{"key":"169_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112688","volume":"266","author":"A Rafiee","year":"2022","unstructured":"Rafiee, A., Haase, M., & Malcolm, A. (2022). Multi-objective bayesian hull form optimisation for high-speed craft. Ocean Engineering, 266, 112688.","journal-title":"Ocean Engineering"},{"key":"169_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.113321","volume":"267","author":"X Liu","year":"2023","unstructured":"Liu, X., Wan, D., & Lei, L. (2023). Multi-fidelity model and reduced-order method for comprehensive hydrodynamic performance optimization and prediction of jbc ship. Ocean Engineering, 267, 113321.","journal-title":"Ocean Engineering"},{"key":"169_CR35","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.3390\/jmse9101107","volume":"9","author":"D Villa","year":"2021","unstructured":"Villa, D., Furcas, F., Pralits, J. O., Vernengo, G., & Gaggero, S. (2021). An effective mesh deformation approach for hull shape design by optimization. Journal of Marine Science and Engineering, 9, 1107.","journal-title":"Journal of Marine Science and Engineering"},{"key":"169_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109406","volume":"235","author":"C \u00c7elik","year":"2021","unstructured":"\u00c7elik, C., Dan\u0131\u015fman, D. B., Khan, S., & Kaklis, P. (2021). A reduced order data-driven method for resistance prediction and shape optimization of hull vane. Ocean Engineering, 235, 109406.","journal-title":"Ocean Engineering"},{"key":"169_CR37","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.apor.2017.01.007","volume":"63","author":"J Wu","year":"2017","unstructured":"Wu, J., Liu, X., Zhao, M., & Wan, D. (2017). Neumann-michell theory-based multi-objective optimization of hull form for a naval surface combatant. Applied Ocean Research, 63, 129\u2013141.","journal-title":"Applied Ocean Research"},{"key":"169_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2021.102861","volume":"116","author":"X Liu","year":"2021","unstructured":"Liu, X., Zhao, W., & Wan, D. (2021). Hull form optimization based on calm-water wave drag with or without generating bulbous bow. Applied Ocean Research, 116, 102861.","journal-title":"Applied Ocean Research"},{"key":"169_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.110376","volume":"244","author":"Z Liu","year":"2022","unstructured":"Liu, Z., Zhao, W., & Wan, D. (2022). Resistance and wake distortion optimization of jbc considering ship-propeller interaction. Ocean Engineering, 244, 110376.","journal-title":"Ocean Engineering"},{"key":"169_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2024.107320","volume":"297","author":"Y Ao","year":"2024","unstructured":"Ao, Y., Duan, H., & Li, S. (2024). An integrated-hull design assisted by artificial intelligence-aided design method. Computers & Structures, 297, 107320.","journal-title":"Computers & Structures"},{"key":"169_CR41","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1109\/LWC.2022.3149007","volume":"11","author":"C Peng","year":"2022","unstructured":"Peng, C., Huang, X., Wu, Y., & Kang, J. (2022). Constrained multi-objective optimization for uav-enabled mobile edge computing: Offloading optimization and path planning. IEEE Wireless Communications Letters, 11, 861\u2013865.","journal-title":"IEEE Wireless Communications Letters"},{"key":"169_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112000","volume":"260","author":"S Wang","year":"2022","unstructured":"Wang, S., Duan, F., Li, Y., Xia, Y., & Li, Z. (2022). An improved radial basis function for marine vehicle hull form representation and optimization. Ocean Engineering, 260, 112000.","journal-title":"Ocean Engineering"},{"key":"169_CR43","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1007\/s42241-018-0079-5","volume":"30","author":"L Yang","year":"2018","unstructured":"Yang, L., Li, S.-Z., Zhao, F., & Ni, Q.-J. (2018). An integrated optimization design of a fishing ship hullform at different speeds. Journal of Hydrodynamics, 30, 1174\u20131181.","journal-title":"Journal of Hydrodynamics"},{"key":"169_CR44","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s00773-019-00640-5","volume":"25","author":"Q Ni","year":"2020","unstructured":"Ni, Q., Ruan, W., Li, S., & Zhao, F. (2020). Multiple speed integrated optimization design for a swath using sbd technique. Journal of Marine Science and Technology, 25, 185\u2013195.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR45","doi-asserted-by":"crossref","unstructured":"Li, S., Zhu, F., Hou, X. & Ni, Q. (2022). Application of mesh deformation and adaptive method in hullform design optimization. Journal of Marine Science and Technology, 1\u201310.","DOI":"10.1007\/s00773-021-00851-9"},{"key":"169_CR46","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.110352","volume":"244","author":"A Hamed","year":"2022","unstructured":"Hamed, A. (2022). Multi-objective optimization method of trimaran hull form for resistance reduction and propeller intake flow improvement. Ocean Engineering, 244, 110352.","journal-title":"Ocean Engineering"},{"key":"169_CR47","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s00773-020-00756-z","volume":"26","author":"P Wang","year":"2021","unstructured":"Wang, P., Chen, Z., & Feng, Y. (2021). Many-objective optimization for a deep-sea aquaculture vessel based on an improved rbf neural network surrogate model. Journal of Marine Science and Technology, 26, 582\u2013605.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109208","volume":"233","author":"L Zha","year":"2021","unstructured":"Zha, L., Zhu, R., Hong, L., & Huang, S. (2021). Hull form optimization for reduced calm-water resistance and improved vertical motion performance in irregular head waves. Ocean Engineering, 233, 109208.","journal-title":"Ocean Engineering"},{"key":"169_CR49","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.oceaneng.2018.01.109","volume":"153","author":"X Cheng","year":"2018","unstructured":"Cheng, X., Feng, B., Liu, Z., & Chang, H. (2018). Hull surface modification for ship resistance performance optimization based on delaunay triangulation. Ocean Engineering, 153, 333\u2013344.","journal-title":"Ocean Engineering"},{"key":"169_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112355","volume":"263","author":"Y Ichinose","year":"2022","unstructured":"Ichinose, Y. (2022). Method involving shape-morphing of multiple hull forms aimed at organizing and visualizing the propulsive performance of optimal ship designs. Ocean Engineering, 263, 112355.","journal-title":"Ocean Engineering"},{"key":"169_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2019.106852","volume":"197","author":"D D\u2019Agostino","year":"2020","unstructured":"D\u2019Agostino, D., Serani, A., & Diez, M. (2020). Design-space assessment and dimensionality reduction: An off-line method for shape reparameterization in simulation-based optimization. Ocean Engineering, 197, 106852.","journal-title":"Ocean Engineering"},{"key":"169_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2021.102812","volume":"114","author":"Q Zheng","year":"2021","unstructured":"Zheng, Q., Feng, B.-W., Chang, H.-C., & Liu, Z.-Y. (2021). Dynamic space reduction optimization framework and its application in hull form optimization. Applied Ocean Research, 114, 102812.","journal-title":"Applied Ocean Research"},{"key":"169_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.cad.2022.103327","volume":"150","author":"S Khan","year":"2022","unstructured":"Khan, S., Kaklis, P., Serani, A., Diez, M., & Kostas, K. (2022). Shape-supervised dimension reduction: Extracting geometry and physics associated features with geometric moments. Computer-Aided Design, 150, 103327.","journal-title":"Computer-Aided Design"},{"key":"169_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109680","volume":"237","author":"X Liu","year":"2021","unstructured":"Liu, X., Zhao, W., & Wan, D. (2021). Linear reduced order method for design-space dimensionality reduction and flow-field learning in hull form optimization. Ocean Engineering, 237, 109680.","journal-title":"Ocean Engineering"},{"key":"169_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112108","volume":"262","author":"Z Qiang","year":"2022","unstructured":"Qiang, Z., Bai-Wei, F., Zu-Yuan, L., Hai-Chao, C., & Xiao, W. (2022). Optimization method for hierarchical space reduction method and its application in hull form optimization. Ocean Engineering, 262, 112108.","journal-title":"Ocean Engineering"},{"key":"169_CR56","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.1016\/j.cma.2014.10.042","volume":"283","author":"M Diez","year":"2015","unstructured":"Diez, M., Campana, E. F., & Stern, F. (2015). Design-space dimensionality reduction in shape optimization by karhunen-lo\u00e8ve expansion. Computer Methods in Applied Mechanics and Engineering, 283, 1525\u20131544.","journal-title":"Computer Methods in Applied Mechanics and Engineering"},{"key":"169_CR57","doi-asserted-by":"publisher","first-page":"2245","DOI":"10.1007\/s00366-021-01375-x","volume":"38","author":"A Serani","year":"2022","unstructured":"Serani, A., Stern, F., Campana, E. F., & Diez, M. (2022). Hull-form stochastic optimization via computational-cost reduction methods. Engineering with Computers, 38, 2245\u20132269.","journal-title":"Engineering with Computers"},{"key":"169_CR58","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1080\/0305215X.2014.895340","volume":"47","author":"X Chen","year":"2015","unstructured":"Chen, X., et al. (2015). High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm. Engineering Optimization, 47, 473\u2013494.","journal-title":"Engineering Optimization"},{"key":"169_CR59","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00773-013-0235-0","volume":"19","author":"M Diez","year":"2014","unstructured":"Diez, M., He, W., Campana, E. F., & Stern, F. (2014). Uncertainty quantification of delft catamaran resistance, sinkage and trim for variable froude number and geometry using metamodels, quadrature and karhunen-lo\u00e8ve expansion. Journal of Marine Science and Technology, 19, 143\u2013169.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR60","doi-asserted-by":"publisher","first-page":"230","DOI":"10.3390\/jmse11010230","volume":"11","author":"H Chang","year":"2023","unstructured":"Chang, H., et al. (2023). Research on the karhunen-lo\u00e8ve transform method and its application to hull form optimization. Journal of Marine Science and Engineering, 11, 230.","journal-title":"Journal of Marine Science and Engineering"},{"key":"169_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.117604","volume":"302","author":"Y Zhang","year":"2024","unstructured":"Zhang, Y., Ma, N., Gu, X., & Shi, Q. (2024). Geometric space construction method combined of a spline-skinning based geometric variation method and pca dimensionality reduction for ship hull form optimization. Ocean Engineering, 302, 117604.","journal-title":"Ocean Engineering"},{"key":"169_CR62","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.114502","volume":"279","author":"P Wang","year":"2023","unstructured":"Wang, P., Feng, Y., Chen, Z., & Dai, Y. (2023). Study of a hull form optimization system based on a gaussian process regression algorithm and an adaptive sampling strategy, part i: Single-objective optimization. Ocean Engineering, 279, 114502.","journal-title":"Ocean Engineering"},{"key":"169_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.115501","volume":"286","author":"P Wang","year":"2023","unstructured":"Wang, P., Feng, Y., Chen, Z., & Dai, Y. (2023). Study of a hull form optimization system based on a gaussian process regression algorithm and an adaptive sampling strategy, part ii: Multi-objective optimization. Ocean Engineering, 286, 115501.","journal-title":"Ocean Engineering"},{"key":"169_CR64","doi-asserted-by":"publisher","first-page":"3685","DOI":"10.1007\/s00500-017-2590-y","volume":"22","author":"Z Zhou","year":"2018","unstructured":"Zhou, Z., & Zhu, S. (2018). Kernel-based multiobjective clustering algorithm with automatic attribute weighting. Soft Computing, 22, 3685\u20133709.","journal-title":"Soft Computing"},{"key":"169_CR65","doi-asserted-by":"publisher","first-page":"7776","DOI":"10.1109\/TCYB.2021.3051078","volume":"52","author":"S Zhu","year":"2022","unstructured":"Zhu, S., Xu, L., Goodman, E. D., & Lu, Z. (2022). A new many-objective evolutionary algorithm based on generalized pareto dominance. IEEE Transactions on Cybernetics, 52, 7776\u20137790.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"169_CR66","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.eswa.2017.12.013","volume":"96","author":"S Zhu","year":"2018","unstructured":"Zhu, S., & Xu, L. (2018). Many-objective fuzzy centroids clustering algorithm for categorical data. Expert Systems with Applications, 96, 230\u2013248.","journal-title":"Expert Systems with Applications"},{"issue":"105018","key":"169_CR67","first-page":"1","volume":"188","author":"S Zhu","year":"2020","unstructured":"Zhu, S., Xu, L., & Goodman, E. D. (2020). Evolutionary multi-objective automatic clustering enhanced with quality metrics and ensemble strategy. Knowledge-Based Systems, 188(105018), 1\u201321.","journal-title":"Knowledge-Based Systems"},{"key":"169_CR68","doi-asserted-by":"publisher","first-page":"9846","DOI":"10.1109\/TCYB.2021.3081988","volume":"52","author":"S Zhu","year":"2022","unstructured":"Zhu, S., Xu, L., & Goodman, E. D. (2022). Hierarchical topology-based cluster representation for scalable evolutionary multiobjective clustering. IEEE Transactions on Cybernetics, 52, 9846\u20139860.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"169_CR69","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Transactions on Evolutionary Computation, 6, 182\u2013197.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"169_CR70","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., & Li, H. (2007). Moea\/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11, 712\u2013731.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"169_CR71","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1109\/TEVC.2004.826067","volume":"8","author":"CAC Coello","year":"2004","unstructured":"Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8, 256\u2013279.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"169_CR72","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2013","unstructured":"Deb, K., & Jain, H. (2013). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18, 577\u2013601.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"169_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.110239","volume":"243","author":"X Liu","year":"2022","unstructured":"Liu, X., Zhao, W., & Wan, D. (2022). Multi-fidelity co-kriging surrogate model for ship hull form optimization. Ocean Engineering, 243, 110239.","journal-title":"Ocean Engineering"},{"key":"169_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.113015","volume":"266","author":"Q Wen","year":"2022","unstructured":"Wen, Q., Feng, R., An, X., Chen, Y., & Huang, H. (2022). Optimal design of an autonomous underwater helicopter\u2019s shape based on combinatorial optimization strategy. Ocean Engineering, 266, 113015.","journal-title":"Ocean Engineering"},{"key":"169_CR75","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.1109\/TMECH.2022.3143125","volume":"27","author":"M Yang","year":"2022","unstructured":"Yang, M., Wang, Y., Liang, Y., & Wang, C. (2022). A new approach to system design optimization of underwater gliders. IEEE\/ASME Transactions on Mechatronics, 27, 3494\u20133505.","journal-title":"IEEE\/ASME Transactions on Mechatronics"},{"key":"169_CR76","doi-asserted-by":"publisher","first-page":"672","DOI":"10.1080\/0305215X.2015.1031660","volume":"48","author":"F Huang","year":"2016","unstructured":"Huang, F., Wang, L., & Yang, C. (2016). A new improved artificial bee colony algorithm for ship hull form optimization. Engineering Optimization, 48, 672\u2013686.","journal-title":"Engineering Optimization"},{"key":"169_CR77","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109438","volume":"236","author":"P Wang","year":"2021","unstructured":"Wang, P., Wang, F., Chen, Z., & Dai, Y. (2021). Aerodynamic optimization of a luxury cruise ship based on a many-objective optimization system. Ocean Engineering, 236, 109438.","journal-title":"Ocean Engineering"},{"key":"169_CR78","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.3390\/jmse10111735","volume":"10","author":"S Zhang","year":"2022","unstructured":"Zhang, S. (2022). Research on the deep learning technology in the hull form optimization problem. Journal of Marine Science and Engineering, 10, 1735.","journal-title":"Journal of Marine Science and Engineering"},{"key":"169_CR79","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s13344-021-0055-7","volume":"35","author":"J-H Wu","year":"2021","unstructured":"Wu, J.-H., Zhen, X.-W., Liu, G., & Huang, Y. (2021). Uncertain multidisciplinary design optimization on next generation subsea production system by using surrogate model and interval method. China Ocean Engineering, 35, 609\u2013621.","journal-title":"China Ocean Engineering"},{"key":"169_CR80","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1016\/j.oceaneng.2010.03.010","volume":"37","author":"M Diez","year":"2010","unstructured":"Diez, M., & Peri, D. (2010). Robust optimization for ship conceptual design. Ocean Engineering, 37, 966\u2013977.","journal-title":"Ocean Engineering"},{"key":"169_CR81","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2019.101860","volume":"90","author":"X Wei","year":"2019","unstructured":"Wei, X., Chang, H., Feng, B., Liu, Z., & Huang, C. (2019). Hull form reliability-based robust design optimization combining polynomial chaos expansion and maximum entropy method. Applied Ocean Research, 90, 101860.","journal-title":"Applied Ocean Research"},{"key":"169_CR82","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1016\/j.asoc.2016.08.028","volume":"49","author":"A Serani","year":"2016","unstructured":"Serani, A., et al. (2016). Parameter selection in synchronous and asynchronous deterministic particle swarm optimization for ship hydrodynamics problems. Applied Soft Computing, 49, 313\u2013334.","journal-title":"Applied Soft Computing"},{"key":"169_CR83","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/s00773-018-0543-5","volume":"24","author":"X Cheng","year":"2019","unstructured":"Cheng, X., Feng, B., Chang, H., Liu, Z., & Zhan, C. (2019). Multi-objective optimisation of ship resistance performance based on cfd. Journal of Marine Science and Technology, 24, 152\u2013165.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR84","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2019.106346","volume":"189","author":"S Skoupas","year":"2019","unstructured":"Skoupas, S., Zaraphonitis, G., & Papanikolaou, A. (2019). Parametric design and optimisation of high-speed ro-ro passenger ships. Ocean Engineering, 189, 106346.","journal-title":"Ocean Engineering"},{"key":"169_CR85","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112262","volume":"263","author":"Y Wan","year":"2022","unstructured":"Wan, Y., et al. (2022). Interval optimization design of a submersible surface ship form considering the uncertainty of surrogate model. Ocean Engineering, 263, 112262.","journal-title":"Ocean Engineering"},{"key":"169_CR86","doi-asserted-by":"publisher","first-page":"2050008","DOI":"10.1142\/S0219876220500085","volume":"17","author":"A Miao","year":"2020","unstructured":"Miao, A., & Wan, D. (2020). Hull form optimization based on an nm+ cfd integrated method for kcs. International Journal of Computational Methods, 17, 2050008.","journal-title":"International Journal of Computational Methods"},{"key":"169_CR87","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112513","volume":"264","author":"G Guan","year":"2022","unstructured":"Guan, G., Zhuang, Z., Yang, Q., Wang, P., & Jin, S. (2022). Hull form optimization design of swath with combination evaluations of resistance and seakeeping performance. Ocean Engineering, 264, 112513.","journal-title":"Ocean Engineering"},{"key":"169_CR88","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2019.106755","volume":"195","author":"J Guo","year":"2020","unstructured":"Guo, J., Zhang, Y., Chen, Z., & Feng, Y. (2020). Cfd-based multi-objective optimization of a waterjet-propelled trimaran. Ocean Engineering, 195, 106755.","journal-title":"Ocean Engineering"},{"key":"169_CR89","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1177\/0954405417752527","volume":"233","author":"Y-W Jung","year":"2019","unstructured":"Jung, Y.-W., & Kim, Y. (2019). Hull form optimization in the conceptual design stage considering operational efficiency in waves. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 233, 745\u2013759.","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment"},{"key":"169_CR90","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.oceaneng.2018.07.062","volume":"166","author":"Y Feng","year":"2018","unstructured":"Feng, Y., et al. (2018). Multidisciplinary optimization of an offshore aquaculture vessel hull form based on the support vector regression surrogate model. Ocean Engineering, 166, 145\u2013158.","journal-title":"Ocean Engineering"},{"key":"169_CR91","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112077","volume":"260","author":"Y Mao","year":"2022","unstructured":"Mao, Y., Wang, T., Duan, M., & Men, H. (2022). Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model. Ocean Engineering, 260, 112077.","journal-title":"Ocean Engineering"},{"key":"169_CR92","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s00773-016-0399-5","volume":"22","author":"X Liu","year":"2017","unstructured":"Liu, X., Yuan, Q., Zhao, M., Cui, W., & Ge, T. (2017). Multiple objective multidisciplinary design optimization of heavier-than-water underwater vehicle using cfd and approximation model. Journal of Marine Science and Technology, 22, 135\u2013148.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR93","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1016\/j.advengsoft.2018.05.010","volume":"123","author":"Y Lin","year":"2018","unstructured":"Lin, Y., He, J., & Li, K. (2018). Hull form design optimization of twin-skeg fishing vessel for minimum resistance based on surrogate model. Advances in Engineering Software, 123, 38\u201350.","journal-title":"Advances in Engineering Software"},{"key":"169_CR94","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1080\/09377255.2020.1802165","volume":"68","author":"M Mittendorf","year":"2021","unstructured":"Mittendorf, M., & Papanikolaou, A. D. (2021). Hydrodynamic hull form optimization of fast catamarans using surrogate models. Ship Technology Research, 68, 14\u201326.","journal-title":"Ship Technology Research"},{"key":"169_CR95","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2021.109038","volume":"230","author":"X Tian","year":"2021","unstructured":"Tian, X., et al. (2021). Multi-objective optimization of the hull form for the semi-submersible medical platform. Ocean Engineering, 230, 109038.","journal-title":"Ocean Engineering"},{"key":"169_CR96","doi-asserted-by":"publisher","first-page":"2794","DOI":"10.1109\/TSMC.2015.2390592","volume":"47","author":"K Alam","year":"2015","unstructured":"Alam, K., Ray, T., & Anavatti, S. G. (2015). Design optimization of an unmanned underwater vehicle using low-and high-fidelity models. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47, 2794\u20132808.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"key":"169_CR97","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1016\/j.asoc.2017.05.013","volume":"58","author":"R Pellegrini","year":"2017","unstructured":"Pellegrini, R., et al. (2017). Formulation and parameter selection of multi-objective deterministic particle swarm for simulation-based optimization. Applied Soft Computing, 58, 714\u2013731.","journal-title":"Applied Soft Computing"},{"key":"169_CR98","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s00158-018-2136-7","volume":"59","author":"H Zakerdoost","year":"2019","unstructured":"Zakerdoost, H., & Ghassemi, H. (2019). A multi-level optimization technique based on fuel consumption and energy index in early-stage ship design. Structural and Multidisciplinary Optimization, 59, 1417\u20131438.","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"169_CR99","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.115124","volume":"283","author":"C Jiang","year":"2023","unstructured":"Jiang, C., Yang, S., Nie, P., & Xiang, X. (2023). Multi-objective structural profile optimization of ships based on improved artificial bee colony algorithm and structural component library. Ocean Engineering, 283, 115124.","journal-title":"Ocean Engineering"},{"key":"169_CR100","doi-asserted-by":"publisher","first-page":"478","DOI":"10.3390\/jmse9050478","volume":"9","author":"H Chen","year":"2021","unstructured":"Chen, H., Li, W., Cui, W., Yang, P., & Chen, L. (2021). Multi-objective multidisciplinary design optimization of a robotic fish system. Journal of marine science and engineering, 9, 478.","journal-title":"Journal of marine science and engineering"},{"key":"169_CR101","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2021.102729","volume":"113","author":"A Nazemian","year":"2021","unstructured":"Nazemian, A., & Ghadimi, P. (2021). Cfd-based optimization of a displacement trimaran hull for improving its calm water and wavy condition resistance. Applied Ocean Research, 113, 102729.","journal-title":"Applied Ocean Research"},{"key":"169_CR102","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s00773-007-0264-7","volume":"13","author":"Y Tahara","year":"2008","unstructured":"Tahara, Y., Peri, D., Campana, E. F., & Stern, F. (2008). Computational fluid dynamics-based multiobjective optimization of a surface combatant using a global optimization method. Journal of Marine Science and Technology, 13, 95\u2013116.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR103","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1007\/s00773-011-0137-y","volume":"16","author":"Y Tahara","year":"2011","unstructured":"Tahara, Y., Peri, D., Campana, E. F., & Stern, F. (2011). Single-and multiobjective design optimization of a fast multihull ship: numerical and experimental results. Journal of Marine Science and Technology, 16, 412\u2013433.","journal-title":"Journal of Marine Science and Technology"},{"key":"169_CR104","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5957\/jsr.2016.60.1.1","volume":"60","author":"H Chang","year":"2016","unstructured":"Chang, H., Cheng, X., Liu, Z., Feng, B., & Zhan, C. (2016). Sample selection method for ship resistance performance optimization based on approximated model. Journal of Ship Research, 60, 1\u201313.","journal-title":"Journal of Ship Research"},{"key":"169_CR105","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2022.103156","volume":"123","author":"S Gaggero","year":"2022","unstructured":"Gaggero, S., Vernengo, G., & Villa, D. (2022). A marine propeller design method based on two-fidelity data levels. Applied Ocean Research, 123, 103156.","journal-title":"Applied Ocean Research"},{"key":"169_CR106","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s00158-014-1128-5","volume":"51","author":"S Volpi","year":"2015","unstructured":"Volpi, S., et al. (2015). Development and validation of a dynamic metamodel based on stochastic radial basis functions and uncertainty quantification. Structural and Multidisciplinary Optimization, 51, 347\u2013368.","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"169_CR107","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.112702","volume":"270","author":"X Ouyang","year":"2023","unstructured":"Ouyang, X., et al. (2023). Application of an improved maximum entropy sampling method in hull form optimization. Ocean Engineering, 270, 112702.","journal-title":"Ocean Engineering"},{"key":"169_CR108","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s00158-020-02508-4","volume":"62","author":"Y Ye","year":"2020","unstructured":"Ye, Y., Wang, Z., & Zhang, X. (2020). An optimal pointwise weighted ensemble of surrogates based on minimization of local mean square error. Structural and Multidisciplinary Optimization, 62, 529\u2013542.","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"169_CR109","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.111209","volume":"256","author":"P Renaud","year":"2022","unstructured":"Renaud, P., Sacher, M., & Scolan, Y.-M. (2022). Multi-objective hull form optimization of a swath configuration using surrogate models. Ocean Engineering, 256, 111209.","journal-title":"Ocean Engineering"},{"key":"169_CR110","doi-asserted-by":"publisher","DOI":"10.1016\/j.apor.2020.102087","volume":"97","author":"F Furcas","year":"2020","unstructured":"Furcas, F., Vernengo, G., Villa, D., & Gaggero, S. (2020). Design of wake equalizing ducts using ranse-based sbdo. Applied Ocean Research, 97, 102087.","journal-title":"Applied Ocean Research"},{"key":"169_CR111","doi-asserted-by":"publisher","first-page":"140","DOI":"10.5957\/jsr.2001.45.2.140","volume":"45","author":"D Peri","year":"2001","unstructured":"Peri, D., Rossetti, M., & Campana, E. F. (2001). Design optimization of ship hulls via cfd techniques. Journal of Ship Research, 45, 140\u2013149.","journal-title":"Journal of Ship Research"},{"key":"169_CR112","doi-asserted-by":"crossref","unstructured":"Wang, S. & Kim, Y. (2024). Cfd-based hull optimization in calm water using adaptive grid deformation method. Journal of Hydrodynamics, 1\u201322.","DOI":"10.1007\/s42241-024-0027-5"},{"key":"169_CR113","doi-asserted-by":"publisher","DOI":"10.1016\/j.marstruc.2023.103561","volume":"94","author":"Y Kang","year":"2024","unstructured":"Kang, Y., Pei, Z., Ao, L., & Wu, W. (2024). Reliability-based design optimization of river-sea-going ship based on agent model technology. Marine Structures, 94, 103561.","journal-title":"Marine Structures"},{"key":"169_CR114","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/s00158-016-1423-4","volume":"54","author":"C Leotardi","year":"2016","unstructured":"Leotardi, C., Serani, A., Iemma, U., Campana, E. F., & Diez, M. (2016). A variable-accuracy metamodel-based architecture for global mdo under uncertainty. Structural and Multidisciplinary Optimization, 54, 573\u2013593.","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"169_CR115","doi-asserted-by":"crossref","unstructured":"Diez, M., et\u00a0al. (2015). Multi-objective hydrodynamic optimization of the dtmb 5415 for resistance and seakeeping. In SNAME International Conference on Fast Sea Transportation, D021S005R012 (SNAME).","DOI":"10.5957\/FAST-2015-034"},{"key":"169_CR116","doi-asserted-by":"publisher","first-page":"83","DOI":"10.5957\/jsr.2014.58.2.83","volume":"58","author":"S-Z Li","year":"2014","unstructured":"Li, S.-Z., Zhao, F., & Ni, Q.-J. (2014). Bow and stern shape integrated optimization for a full ship by a simulation-based design technique. Journal of Ship Research, 58, 83\u201396.","journal-title":"Journal of Ship Research"},{"key":"169_CR117","unstructured":"Pellegrini, R., et\u00a0al. (2022). A multi-fidelity active learning method for global design optimization problems with noisy evaluations. Engineering with Computers, 1\u201324."},{"key":"169_CR118","doi-asserted-by":"publisher","first-page":"2209","DOI":"10.1007\/s00366-021-01588-0","volume":"39","author":"C Piazzola","year":"2023","unstructured":"Piazzola, C., et al. (2023). Comparing multi-index stochastic collocation and multi-fidelity stochastic radial basis functions for forward uncertainty quantification of ship resistance. Engineering with Computers, 39, 2209\u20132237.","journal-title":"Engineering with Computers"},{"key":"169_CR119","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2022.113529","volume":"271","author":"R Pellegrini","year":"2023","unstructured":"Pellegrini, R., et al. (2023). Multi-fidelity hydrodynamic analysis of an autonomous surface vehicle at surveying speed in deep water subject to variable payload. Ocean Engineering, 271, 113529.","journal-title":"Ocean Engineering"},{"key":"169_CR120","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/s00158-006-0051-9","volume":"33","author":"T Goel","year":"2007","unstructured":"Goel, T., Haftka, R. T., Shyy, W., & Queipo, N. V. (2007). Ensemble of surrogates. Structural and Multidisciplinary Optimization, 33, 199\u2013216.","journal-title":"Structural and Multidisciplinary Optimization"},{"key":"169_CR121","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2020.108212","volume":"219","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Wang, Z., Ling, H., & Zhu, X. (2021). Kriging-based shape optimization framework for blended-wing-body underwater glider with nurbs-based parametrization. Ocean Engineering, 219, 108212.","journal-title":"Ocean Engineering"},{"key":"169_CR122","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.oceaneng.2017.05.018","volume":"140","author":"YH Hou","year":"2017","unstructured":"Hou, Y. H. (2017). Hull form uncertainty optimization design for minimum eeoi with influence of different speed perturbation types. Ocean Engineering, 140, 66\u201372.","journal-title":"Ocean Engineering"},{"key":"169_CR123","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1080\/19942060.2021.1915875","volume":"15","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Tezdogan, T., Zhang, B., & Lin, L. (2021). Research on the hull form optimization using the surrogate models. Engineering Applications of Computational Fluid Mechanics, 15, 747\u2013761.","journal-title":"Engineering Applications of Computational Fluid Mechanics"},{"key":"169_CR124","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.2514\/1.J051895","volume":"51","author":"JR Martins","year":"2013","unstructured":"Martins, J. R., & Lambe, A. B. (2013). Multidisciplinary design optimization: a survey of architectures. AIAA Journal, 51, 2049\u20132075.","journal-title":"AIAA Journal"},{"key":"169_CR125","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2023.114554","volume":"279","author":"H Li","year":"2023","unstructured":"Li, H., Wei, X., Liu, Z., Feng, B., & Zheng, Q. (2023). Ship design optimization with mixed uncertainty based on evidence theory. Ocean Engineering, 279, 114554.","journal-title":"Ocean Engineering"},{"key":"169_CR126","unstructured":"Diez, M., Peri, D., Fasano, G. & Campana, E.\u00a0F. (2010). Multidisciplinary robust optimization for ship design. In 28th symposium on naval hydrodynamic, Pasadena, Caloifornia, USA."},{"key":"169_CR127","doi-asserted-by":"publisher","first-page":"1012","DOI":"10.1109\/TCYB.2018.2794503","volume":"49","author":"D Guo","year":"2018","unstructured":"Guo, D., Jin, Y., Ding, J., & Chai, T. (2018). Heterogeneous ensemble-based infill criterion for evolutionary multiobjective optimization of expensive problems. IEEE transactions on cybernetics, 49, 1012\u20131025.","journal-title":"IEEE transactions on cybernetics"},{"key":"169_CR128","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1109\/TEVC.2021.3113923","volume":"26","author":"M Cui","year":"2022","unstructured":"Cui, M., Li, L., Zhou, M., & Abusorrah, A. (2022). Surrogate-assisted autoencoder-embedded evolutionary optimization algorithm to solve high-dimensional expensive problems. IEEE Transactions on Evolutionary Computation, 26, 676\u2013689.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"169_CR129","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1109\/TEVC.2018.2869001","volume":"23","author":"Y Jin","year":"2018","unstructured":"Jin, Y., Wang, H., Chugh, T., Guo, D., & Miettinen, K. (2018). Data-driven evolutionary optimization: An overview and case studies. IEEE Transactions on Evolutionary Computation, 23, 442\u2013458.","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"169_CR130","doi-asserted-by":"publisher","first-page":"4405","DOI":"10.1109\/TNNLS.2019.2957109","volume":"31","author":"H Liu","year":"2020","unstructured":"Liu, H., Ong, Y.-S., Shen, X., & Cai, J. (2020). When gaussian process meets big data: A review of scalable gps. IEEE transactions on neural networks and learning systems, 31, 4405\u20134423.","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"169_CR131","doi-asserted-by":"publisher","first-page":"1952","DOI":"10.1109\/JAS.2022.105425","volume":"9","author":"M Cui","year":"2022","unstructured":"Cui, M., et al. (2022). A bi-population cooperative optimization algorithm assisted by an autoencoder for medium-scale expensive problems. IEEE\/CAA Journal of Automatica Sinica, 9, 1952\u20131966.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"169_CR132","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1109\/TCYB.2018.2869674","volume":"50","author":"H Wang","year":"2018","unstructured":"Wang, H., & Jin, Y. (2018). A random forest-assisted evolutionary algorithm for data-driven constrained multiobjective combinatorial optimization of trauma systems. IEEE transactions on cybernetics, 50, 536\u2013549.","journal-title":"IEEE transactions on cybernetics"},{"key":"169_CR133","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.energy.2019.06.115","volume":"183","author":"D Bhattacharjee","year":"2019","unstructured":"Bhattacharjee, D., Ghosh, T., Bhola, P., Martinsen, K., & Dan, P. K. (2019). Data-driven surrogate assisted evolutionary optimization of hybrid powertrain for improved fuel economy and performance. Energy, 183, 235\u2013248.","journal-title":"Energy"},{"key":"169_CR134","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2022.118961","volume":"315","author":"Q Yuan","year":"2022","unstructured":"Yuan, Q., Ye, Y., Tang, Y., Liu, Y., & Strbac, G. (2022). A novel deep-learning based surrogate modeling of stochastic electric vehicle traffic user equilibrium in low-carbon electricity-transportation nexus. Applied Energy, 315, 118961.","journal-title":"Applied Energy"},{"key":"169_CR135","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1109\/JAS.2024.124320","volume":"11","author":"M Zhou","year":"2024","unstructured":"Zhou, M., et al. (2024). Evolutionary optimization methods for high-dimensional expensive problems: A survey. IEEE\/CAA Journal of Automatica Sinica, 11, 1092\u20131105.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"169_CR136","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.116810","volume":"295","author":"Z Zhou","year":"2024","unstructured":"Zhou, Z., et al. (2024). Indicator designing for performance evaluation of collision avoidance algorithms programs on autonomous ships. Ocean Engineering, 295, 116810.","journal-title":"Ocean Engineering"},{"key":"169_CR137","doi-asserted-by":"crossref","unstructured":"Wang, N., Song, J. & Dong, Q. (2024). Structural design of a wave-adaptive unmanned quadramaran with independent suspension. IEEE Transactions on Intelligent Transportation Systems.","DOI":"10.1109\/TITS.2024.3375278"}],"container-title":["Journal of Membrane Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00169-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41965-024-00169-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-024-00169-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T04:16:13Z","timestamp":1733199373000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41965-024-00169-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":137,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["169"],"URL":"https:\/\/doi.org\/10.1007\/s41965-024-00169-6","relation":{},"ISSN":["2523-8906","2523-8914"],"issn-type":[{"value":"2523-8906","type":"print"},{"value":"2523-8914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]},"assertion":[{"value":"9 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}