Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design
This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By integrating parametric optimization with AIdriven metamodeling, the research compares four multi-objective optimization algorithms-Non-Sorted G...
Uložené v:
| Vydané v: | Mechanics based design of structures and machines Ročník 53; číslo 8; s. 5822 - 5849 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Taylor & Francis
03.08.2025
|
| Predmet: | |
| ISSN: | 1539-7734, 1539-7742 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By integrating parametric optimization with AIdriven metamodeling, the research compares four multi-objective optimization algorithms-Non-Sorted Genetic Algorithm II, Multi-objective Lichtenberg Algorithm, Multi-objective Sunflower Optimization, and Multi-objective Particle Swarm Optimization-aiming to minimize chassis mass and maximize stiffness. The results show that AI-driven metamodeling significantly reduces computational cost, cutting optimization time by over 99%, while maintaining accuracy comparable to direct Finite Element simulations. This work provides a framework for enhanced automotive and structural optimization. |
|---|---|
| AbstractList | This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By integrating parametric optimization with AIdriven metamodeling, the research compares four multi-objective optimization algorithms-Non-Sorted Genetic Algorithm II, Multi-objective Lichtenberg Algorithm, Multi-objective Sunflower Optimization, and Multi-objective Particle Swarm Optimization-aiming to minimize chassis mass and maximize stiffness. The results show that AI-driven metamodeling significantly reduces computational cost, cutting optimization time by over 99%, while maintaining accuracy comparable to direct Finite Element simulations. This work provides a framework for enhanced automotive and structural optimization. |
| Author | Bendine, Kouider Gomes, Guilherme Ferreira Pereira, Joao Luiz Junho |
| Author_xml | – sequence: 1 givenname: Guilherme Ferreira surname: Gomes fullname: Gomes, Guilherme Ferreira organization: Mechanical Engineering Institute, Federal University of Itajubá (UNIFEI) – sequence: 2 givenname: Kouider surname: Bendine fullname: Bendine, Kouider organization: Materials Research and Technology (MRT) Department Luxembourg Institute of Science and Technology (LIST) – sequence: 3 givenname: Joao Luiz Junho surname: Pereira fullname: Pereira, Joao Luiz Junho organization: Production and Management Engineering Institute, Federal University of Itajubá (UNIFEI) |
| BookMark | eNp9kMlOwzAQhi1UJMryCEh-gKZ4SeqEExVik5C4wDlyvaSDHDuyXVB5Ih6TlE2cOM1o9C-j7xBNfPAGoVNK5pTU5IxWvBGCl3NGWDVnpViQku6h6e5eCFGyye_OywN0mNIzIYxRwqfo_WHI0MObzBA8ll5jGTNYUCAdBp-Nc9AZr8w5XvrxUGgz5PUolG6bIOFgcb9xGYqwejYqw4vB4U_iDCfZDw58h3uT10Gn2WdJNF00KX12ui5EyOs-YTmMUqNxDjjluFF5E8cvtEnQ-WO0b6VL5uR7HqGn66vHy9vi_uHm7nJ5Xyi2ILlo6rIWmptVpU1TacYsoYrZmtJqwWxJFkJTq1ec67qWjZaWVqQW3OpSCckayo9Q9ZWrYkgpGtsOEXoZty0l7Q53-4O73eFuv3GPvosvH3gbYi9fQ3S6zXLrQrRRegWp5f9HfABZIY35 |
| Cites_doi | 10.1016/j.asoc.2018.06.050 10.1080/0305215X.2020.1839442 10.1016/j.istruc.2023.105271 10.1016/j.engstruct.2018.11.081 10.1037/h0042519 10.1007/3-540-45356-3_83 10.1007/s00158-010-0578-7 10.1145/3615354 10.1109/21.52545 10.1016/j.engappai.2023.107536 10.1016/j.eswa.2021.115939 10.1016/S0166-3615(99)00046-9 10.1016/j.eswa.2020.114522 10.1007/s00366-021-01299-6 10.1007/s10462-017-9605-z 10.1016/j.compstruct.2023.117043 10.1007/978-3-540-28650-9_4 10.1109/MCAS.2006.1688199 10.1016/j.jjimei.2023.100209 10.2307/1266224 10.1016/j.crme.2018.09.003 10.1007/s10618-023-00957-1 10.1016/j.eswa.2023.121549 10.1109/ICNN.1995.488968 10.48550/ARXIV.1706.03762 10.1145/3436893 10.1016/j.engappai.2020.104055 10.1023/A:1022697719738 10.1016/S0169-7161(04)24011-1 10.1515/math-2017-0029 10.1007/s10994-023-06490-y 10.1016/j.compstruc.2021.106508 10.1016/j.neucom.2008.01.031 10.1137/16M1080173 10.1080/0305215X.2024.2349104 10.1080/24705314.2024.2390258 |
| ContentType | Journal Article |
| Copyright | 2025 Taylor & Francis Group, LLC 2025 |
| Copyright_xml | – notice: 2025 Taylor & Francis Group, LLC 2025 |
| DBID | AAYXX CITATION |
| DOI | 10.1080/15397734.2025.2476041 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1539-7742 |
| EndPage | 5849 |
| ExternalDocumentID | 10_1080_15397734_2025_2476041 2476041 |
| Genre | Research Article |
| GrantInformation_xml | – fundername: CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) – fundername: FAPESP (São Paulo Research Foundation grantid: 2023/10419-0; 2022/10683-7 – fundername: FAPEMIG (Fundacão de Amparo à Pesquisa do Estado de Minas Gerais grantid: APQ-00062-24 |
| GroupedDBID | .7F .QJ 0BK 29M 30N 4.4 5GY 5VS AAENE AAGDL AAHIA AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABDBF ABFIM ABHAV ABJNI ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGEJ ACGFS ACTIO ADCVX ADGTB ADXPE AEISY AENEX AEOZL AEPSL AEYOC AFKVX AFRVT AGDLA AGMYJ AHDZW AIJEM AIYEW AJWEG AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO DU5 EAP EBS EST ESX E~A E~B GEVLZ GTTXZ H13 HF~ HZ~ H~P IPNFZ J.P KYCEM LJTGL M4Z NA5 NX~ O9- RIG RNANH ROSJB RTWRZ S-T SNACF TASJS TBQAZ TEN TFL TFT TFW TNC TTHFI TUROJ TUS TWF UT5 UU3 ZGOLN ~S~ AAYXX CITATION |
| ID | FETCH-LOGICAL-c260t-98487d3eb5de95d22f01c2f811562f4067d1fdb33d88a9daf150873fd4c7a2913 |
| IEDL.DBID | TFW |
| ISICitedReferencesCount | 3 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001455470900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1539-7734 |
| IngestDate | Sat Nov 29 07:44:56 EST 2025 Mon Oct 20 23:46:45 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 8 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c260t-98487d3eb5de95d22f01c2f811562f4067d1fdb33d88a9daf150873fd4c7a2913 |
| PageCount | 28 |
| ParticipantIDs | crossref_primary_10_1080_15397734_2025_2476041 informaworld_taylorfrancis_310_1080_15397734_2025_2476041 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-08-03 |
| PublicationDateYYYYMMDD | 2025-08-03 |
| PublicationDate_xml | – month: 08 year: 2025 text: 2025-08-03 day: 03 |
| PublicationDecade | 2020 |
| PublicationTitle | Mechanics based design of structures and machines |
| PublicationYear | 2025 |
| Publisher | Taylor & Francis |
| Publisher_xml | – name: Taylor & Francis |
| References | Mihailidis A. (e_1_3_7_30_1) 2009; 223 e_1_3_7_41_1 e_1_3_7_20_1 e_1_3_7_22_1 e_1_3_7_43_1 e_1_3_7_24_1 e_1_3_7_49_1 Gorishniy Y. (e_1_3_7_19_1) 2021; 34 e_1_3_7_28_1 Faceli K. (e_1_3_7_13_1) 2021 Li H. (e_1_3_7_25_1) 2013; 1 e_1_3_7_51_1 Agarwal P. (e_1_3_7_2_1) 2021; 5 e_1_3_7_32_1 e_1_3_7_11_1 e_1_3_7_34_1 Vapnik V. (e_1_3_7_45_1) 1996; 9 e_1_3_7_36_1 e_1_3_7_15_1 e_1_3_7_38_1 e_1_3_7_17_1 e_1_3_7_4_1 e_1_3_7_6_1 e_1_3_7_8_1 e_1_3_7_40_1 e_1_3_7_21_1 e_1_3_7_44_1 e_1_3_7_23_1 e_1_3_7_42_1 e_1_3_7_48_1 e_1_3_7_27_1 e_1_3_7_46_1 e_1_3_7_29_1 Liu J. (e_1_3_7_26_1) 2018; 2 Doyle E. (e_1_3_7_12_1) 2021; 46 Wang L. (e_1_3_7_47_1) 2018; 2018 e_1_3_7_10_1 e_1_3_7_31_1 e_1_3_7_33_1 e_1_3_7_14_1 e_1_3_7_35_1 e_1_3_7_16_1 e_1_3_7_37_1 e_1_3_7_18_1 e_1_3_7_39_1 Yani I. M. (e_1_3_7_50_1) 2021; 18 e_1_3_7_3_1 e_1_3_7_5_1 e_1_3_7_7_1 e_1_3_7_9_1 |
| References_xml | – ident: e_1_3_7_27_1 doi: 10.1016/j.asoc.2018.06.050 – ident: e_1_3_7_14_1 doi: 10.1080/0305215X.2020.1839442 – ident: e_1_3_7_16_1 – ident: e_1_3_7_4_1 doi: 10.1016/j.istruc.2023.105271 – volume: 2 start-page: 251 issue: 3 year: 2018 ident: e_1_3_7_26_1 article-title: Torsional Stiffness and Vibrational Analysis of Formula Sae Chassis publication-title: Journal of Vibration Testing and System Dynamics – ident: e_1_3_7_17_1 doi: 10.1016/j.engstruct.2018.11.081 – ident: e_1_3_7_41_1 – ident: e_1_3_7_39_1 doi: 10.1037/h0042519 – ident: e_1_3_7_10_1 doi: 10.1007/3-540-45356-3_83 – ident: e_1_3_7_7_1 doi: 10.1007/s00158-010-0578-7 – ident: e_1_3_7_29_1 doi: 10.1145/3615354 – ident: e_1_3_7_37_1 doi: 10.1109/21.52545 – ident: e_1_3_7_11_1 doi: 10.1016/j.engappai.2023.107536 – ident: e_1_3_7_34_1 doi: 10.1016/j.eswa.2021.115939 – ident: e_1_3_7_8_1 doi: 10.1016/S0166-3615(99)00046-9 – ident: e_1_3_7_33_1 doi: 10.1016/j.eswa.2020.114522 – volume: 9 year: 1996 ident: e_1_3_7_45_1 article-title: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing publication-title: Advances in Neural Information Processing Systems – ident: e_1_3_7_9_1 doi: 10.1007/s00366-021-01299-6 – ident: e_1_3_7_21_1 doi: 10.1007/s10462-017-9605-z – ident: e_1_3_7_18_1 doi: 10.1016/j.compstruct.2023.117043 – volume-title: Inteligência artificial: Uma abordagem de aprendizado de máquina year: 2021 ident: e_1_3_7_13_1 – ident: e_1_3_7_38_1 doi: 10.1007/978-3-540-28650-9_4 – ident: e_1_3_7_36_1 doi: 10.1109/MCAS.2006.1688199 – ident: e_1_3_7_48_1 doi: 10.1016/j.jjimei.2023.100209 – ident: e_1_3_7_22_1 doi: 10.2307/1266224 – ident: e_1_3_7_51_1 doi: 10.1016/j.crme.2018.09.003 – ident: e_1_3_7_35_1 doi: 10.1007/s10618-023-00957-1 – volume: 223 start-page: 995 issue: 8 year: 2009 ident: e_1_3_7_30_1 article-title: A Design Approach on Formula Sae Vehicle Chassis publication-title: Journal of Automobile Engineering – ident: e_1_3_7_42_1 doi: 10.1016/j.eswa.2023.121549 – volume: 2018 start-page: 1 year: 2018 ident: e_1_3_7_47_1 article-title: Genetic Algorithm-Based Optimization for the Chassis Design of Electric Vehicles publication-title: Journal of Electrical and Computer Engineering – volume: 5 start-page: 45 year: 2021 ident: e_1_3_7_2_1 article-title: Numerical Optimization of Vehicle Chassis publication-title: International Journal of Mechanical Engineering – ident: e_1_3_7_44_1 – ident: e_1_3_7_23_1 doi: 10.1109/ICNN.1995.488968 – ident: e_1_3_7_46_1 doi: 10.48550/ARXIV.1706.03762 – ident: e_1_3_7_31_1 doi: 10.1145/3436893 – ident: e_1_3_7_32_1 doi: 10.1016/j.engappai.2020.104055 – ident: e_1_3_7_49_1 doi: 10.1023/A:1022697719738 – volume: 34 start-page: 18932 year: 2021 ident: e_1_3_7_19_1 article-title: Revisiting Deep Learning Models for Tabular Data publication-title: Advances in Neural Information Processing Systems – ident: e_1_3_7_43_1 doi: 10.1016/S0169-7161(04)24011-1 – ident: e_1_3_7_6_1 doi: 10.1515/math-2017-0029 – volume: 46 start-page: 112 year: 2021 ident: e_1_3_7_12_1 article-title: Fatigue Life Prediction of Formula Sae Chassis publication-title: Journal of Fatigue Analysis – ident: e_1_3_7_20_1 doi: 10.1007/s10994-023-06490-y – ident: e_1_3_7_15_1 doi: 10.1016/j.compstruc.2021.106508 – ident: e_1_3_7_28_1 doi: 10.1016/j.neucom.2008.01.031 – ident: e_1_3_7_5_1 doi: 10.1137/16M1080173 – volume: 1 start-page: 34 year: 2013 ident: e_1_3_7_25_1 article-title: Formula Sae Chassis Design for Enhanced Aerodynamic Performance publication-title: Journal of Automotive Performance – ident: e_1_3_7_3_1 doi: 10.1080/0305215X.2024.2349104 – ident: e_1_3_7_40_1 – volume: 18 start-page: 8389 year: 2021 ident: e_1_3_7_50_1 article-title: Chassis Load Analysis for Weight Reduction in Formula Student Vehicles publication-title: International Journal of Automotive and Mechanical Engineering – ident: e_1_3_7_24_1 doi: 10.1080/24705314.2024.2390258 |
| SSID | ssj0022103 |
| Score | 2.3749394 |
| Snippet | This study addresses the challenge of structural optimization in Formula SAE chassis, focusing on balancing lightweight design with structural integrity. By... |
| SourceID | crossref informaworld |
| SourceType | Index Database Publisher |
| StartPage | 5822 |
| SubjectTerms | artificial intelligence formula SAE metamodel Multi-objective optimization regression |
| Title | Optimization and artificial intelligence: An in-depth analysis of multi-objective optimization, sampling methods, and regression algorithms applied to structural design |
| URI | https://www.tandfonline.com/doi/abs/10.1080/15397734.2025.2476041 |
| Volume | 53 |
| WOSCitedRecordID | wos001455470900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAWR databaseName: Taylor & Francis Online Journals customDbUrl: eissn: 1539-7742 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0022103 issn: 1539-7734 databaseCode: TFW dateStart: 20030104 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQYoCBN6K85IERA7HjJGZDiIqpMBTBFjl-tEU0qZqI38TP5GynKB1ggTFRfI58d7476_N3CJ2nOtKxlYaYgicktrEmoogUMZzqiNtIcyl9s4l0MMheX8VTiyasW1ilq6FtIIrwe7VzblnUC0TcFTgpZC3MnYhQfknjNLn2V9ch9DvXHPZfvksuKGhYYEwVxA1Z3OH5ScpSdFriLu1Enf7WP_zvNtpsU058G2xkB62YchdtdIgI99DnI-wc0_ZKJoaJsLOoQC6BJx3Wzht8W8ILos2sGcOHgdEEVxZ7ZCKpirewg-KqI_EC19Ih18sRDh2r6ws_ydyMAgwX5nwfVfNJM57WWIbEGDcVDuy2jhkEaw812UfP_fvh3QNpezgQBZVSQ0QGFZFmYAnaCK4ptdeRojaDRDShFrIJMBarC8Z0lkmhpXX89CmzOlappCJiB2i1rEpziHAsuVKJ1UxyHWfKCBDEFRWxkBBmVdJDlwvd5bNA1ZFHLQPqQgW5U0HeqqCHRFfDeePPSGxoaJKzX8ce_WHsMVp3jx5GyE7QKiylOUVr6qOZ1PMzb8BffFLyjg |
| linkProvider | Taylor & Francis |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELYQIAEDb0R5emCsC7HjJGZDiKoIKEsR3SLXj7YIEtRG_CZ-Juc4RekAC6xJbEe-s--h775D6CzWgQ6tNMQMeERCG2oiBoEihlMdcBtoLmXZbCLudpN-X9RrYRys0sXQ1hNFlHe1O9wuGT2DxJ3DKQW3hbmUCOUtGsbRhatdX-Jgax1_fq_9_B10QUjDPGeqIG7MrIrnp2nm7NMce2nN7rQ3_uOPN9F65XXiK68mW2jBZNtorcZFuIM-H-HyeKuqMjGshJ1SeX4JPK4Rd17iqwweEG3eixF86ElNcG5xCU4k-eDFX6I4r83YxFPpwOvZEPum1dNmucjEDD0SF9Z8HeaTcTF6m2LpfWNc5NgT3DpyEKxLtMkuemrf9K47pGrjQBQESwURCQRFmoEyaCO4ptReBIraBHzRiFpwKEBfrB4wppNECi2to6iPmdWhiiUVAdtDi1memX2EQ8mViqxmkuswUUbARFxREQoJllZFDdSaCS9992wdaVCRoM5EkDoRpJUIGkjURZwWZZrE-p4mKft17MEfxp6ilU7v4T69v-3eHaJV96pEFbIjtAjbao7RsvooxtPJSanNX83A9rg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lj9MwELZQQWj3wHNX2-XlA8e6NHacxNwqdisQqPRQRG-R67HbrrZJ1UT7m_iZO45TlB7gAtckY0ee8Tysz98Q8j6FCGKnLbNLmbDYxcDUMjLMSg6RdBFIrZtmE-l0mi0WataiCasWVulraBeIIhpf7Tf3DtwBEfcBNylmLcKfiHA55HGajPzV9YeYOifeyOeTn79rLqxoRKBMVczLHC7x_GmYo_B0RF7aCTuTp__hh5-RJ23OScfBSJ6TB7Z4QU47TIQvya_v6Dq27Z1MihNRb1KBXYJuOrSdH-m4wAcM7K5e44eB0oSWjjbQRFYub4ILpWVnxAGttIeuFysaWlZXg2aSvV0FHC7Oebsq95t6va2oDpkxrUsa6G09NQiFBmtyRn5MruefPrO2iQMzWCrVTGVYEoFAUwCrJHDuRpHhLsNMNOEO0wm0FgdLISDLtALtPEF9KhzEJtVcReKc9IqysBeExloakzgQWkKcGatwIGm4ipXGOGuSPhkedJfvAldHHrUUqAcV5F4FeauCPlFdDed1c0jiQkeTXPxV9vIfZN-Rx7OrSf7ty_TrK3Li3zSQQvGa9HBV7RvyyNzVm2r_trHle_ZC9Wo |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimization+and+artificial+intelligence%3A+An+in-depth+analysis+of+multi-objective+optimization%2C+sampling+methods%2C+and+regression+algorithms+applied+to+structural+design&rft.jtitle=Mechanics+based+design+of+structures+and+machines&rft.au=Gomes%2C+Guilherme+Ferreira&rft.au=Bendine%2C+Kouider&rft.au=Pereira%2C+Joao+Luiz+Junho&rft.date=2025-08-03&rft.issn=1539-7734&rft.eissn=1539-7742&rft.volume=53&rft.issue=8&rft.spage=5822&rft.epage=5849&rft_id=info:doi/10.1080%2F15397734.2025.2476041&rft.externalDBID=n%2Fa&rft.externalDocID=10_1080_15397734_2025_2476041 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1539-7734&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1539-7734&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1539-7734&client=summon |