Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method

Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy conve...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Applied energy Ročník 362; s. 122955
Hlavní autoři:	Neshat, Mehdi, Sergiienko, Nataliia Y., Nezhad, Meysam Majidi, da Silva, Leandro S.P., Amini, Erfan, Marsooli, Reza, Astiaso Garcia, Davide, Mirjalili, Seyedali
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 15.05.2024
Témata:	acceleration algorithms Australia coasts damping decline energy Genetic algorithms Hybrid wave-wind energy system Hybrid wave-wind energy systems Hybrid waves hybrids income longevity Multi-objective optimisation algorithm Multi-objective optimization algorithm Multi-objectives optimization Multiobjective optimization Offshore oil well production Offshore wind turbine Offshore wind turbines Optimization algorithms power generation Power quality Power takeoffs spring Sustainable energy Swarm intelligence algorithms swarms system optimization water power wave energy Wave energy conversion Wave energy converters Wave wind wind Wind energy systems Wind power wind turbine wind turbines Australia Swarm-intelligence algorithms Offshore wind turbine Hybrid wave-wind energy systems Sustainable energy Wave energy converters Multi-objective optimisation algorithm Genetic algorithms
ISSN:	0306-2619, 1872-9118, 1872-9118
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model’s parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. •A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform.•A new combination of chaotic adaptive exploration and dynamic archive is introduced.•Effective and practical discretisation method improved performance of hybrid platform.•The performance of the proposed optimiser is validated on four real sea studies.
AbstractList	Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model’s parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or co-locating several renewable technologies. One example is a hybrid wave-wind energy system that combines offshore wind turbines with wave energy converters (WECs) on a single floating foundation. The design of such systems involves multiple parameters and performance measures, making it a complex, multi-modal, and expensive optimisation problem. This paper proposes a novel, robust and effective multi-objective swarm optimisation method (DMOGWA) to provide a design solution that best compromises between maximising WEC power output and minimising the effect on wind turbine nacelle acceleration. The proposed method uses a chaotic adaptive search strategy with a dynamic archive of non-dominated solutions based on diversity to speed up the convergence rate and enhance the Pareto front quality. Furthermore, a modified exploitation technique (Discretisation Strategy) is proposed to handle the large damping and spring coefficient of the Power Take-off (PTO) search space. To evaluate the efficiency of the proposed method, we compare the DMOGWA with four well-known multi-objective swarm intelligence methods (MOPSO, MALO, MODA, and MOGWA) and four popular evolutionary multi-objective algorithms (NSGA-II, MOEA/D, SPEA-II, and PESA-II) based on four potential deployment sites on the South Coast of Australia. The optimisation results demonstrate the dominance of the DMOGWA compared with the other eight methods in terms of convergence speed and quality of solutions proposed. Furthermore, adjusting the hybrid wave-wind model’s parameters (WEC design and PTO parameters) using the proposed method (DMOGWA) leads to a considerably improved power output (average proximate boost of 138.5%) and a notable decline in wind turbine nacelle acceleration (41%) throughout the entire operational spectrum compared with the other methods. This improvement could lead to millions of dollars in additional income per year over the lifespan of hybrid offshore renewable energy platforms. •A new multiobjective optimisation method proposed and enhanced hybrid wave-wind platform.•A new combination of chaotic adaptive exploration and dynamic archive is introduced.•Effective and practical discretisation method improved performance of hybrid platform.•The performance of the proposed optimiser is validated on four real sea studies.
ArticleNumber	122955
Author	Marsooli, Reza Nezhad, Meysam Majidi Sergiienko, Nataliia Y. Neshat, Mehdi Amini, Erfan da Silva, Leandro S.P. Astiaso Garcia, Davide Mirjalili, Seyedali
Author_xml	– sequence: 1 givenname: Mehdi orcidid: 0000-0002-9537-9513 surname: Neshat fullname: Neshat, Mehdi email: mehdi.neshat@torrens.edu.au, mehdi.neshat@uts.edu.au organization: Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, QLD, 4006, Australia – sequence: 2 givenname: Nataliia Y. orcidid: 0000-0002-3418-398X surname: Sergiienko fullname: Sergiienko, Nataliia Y. email: nataliia.sergiienko@adelaide.edu.au organization: School of Electrical and Mechanical Engineering, University of Adelaide, Adelaide, SA, 5005, Australia – sequence: 3 givenname: Meysam Majidi surname: Nezhad fullname: Nezhad, Meysam Majidi email: meysam.majidi.nezhad@uniroma1.it, meysam.majidi.nezhad@mdu.se organization: Department of Sustainable Energy Systems, Mälardalen University, Västerås, SE 72123, Sweden – sequence: 4 givenname: Leandro S.P. surname: da Silva fullname: da Silva, Leandro S.P. email: ldasilva@delmarsystems.com organization: Delmar Systems, Perth, WA, 6000, Australia – sequence: 5 givenname: Erfan orcidid: 0000-0001-9002-3203 surname: Amini fullname: Amini, Erfan email: eamini@stevens.edu organization: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States – sequence: 6 givenname: Reza surname: Marsooli fullname: Marsooli, Reza email: rmarsool@stevens.edu organization: Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ, United States – sequence: 7 givenname: Davide surname: Astiaso Garcia fullname: Astiaso Garcia, Davide email: davide.astiasogarcia@uniroma1.it organization: Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, Italy – sequence: 8 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali email: ali.Mirjalili@torrens.edu.au organization: Center for Artificial Intelligence Research and optimisation, Torrens University Australia, Brisbane, QLD, 4006, Australia
BackLink	https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-66338$$DView record from Swedish Publication Index (Mälardalens högskola)
BookMark	eNqFkc1u1DAUhS1UJKaFV0BesiCDfxLHkVhQteVHqtRNy9Zy7JuJR0kcbGdG8wo8NW4DLLrp6kpX3zn36pxzdDb5CRB6T8mWEio-7bd6hgnC7rRlhJVbylhTVa_QhsqaFQ2l8gxtCCeiYII2b9B5jHtCCKOMbNDvm6nXk3HTDqce8Ayh82HMG8C-w_2pDc7ioz5AcXSTxesdHE8xwRizJPhl12ONOx0T1pnQVs_JHQCbXvvkDB6XIbnCt3swT_t41GHEPkOjizo5P-ERUu_tW_S600OEd3_nBXr4enN_9b24vfv24-rytjAlJ6kwohGGNbIEwQjXlgNIKiw0srYgeQVMlLRsOSGNEUJUXZ7MGNLKstKcW36BPq6-8Qjz0qo5uFGHk_LaqWv381L5sFOj7ZUQnMuMf1jxOfhfC8Sk8t8GhkFP4JeoOK14LeumqjMqVtQEH2OA7r83JeqxK7VX_7pSj12ptass_PxMaFx6yiYF7YaX5V9WOeTYDg6CisZB7tC6kFNX1ruXLP4Am5u6Lg
CitedBy_id	crossref_primary_10_1016_j_ref_2024_100624 crossref_primary_10_3390_su16219333 crossref_primary_10_1016_j_energy_2025_136697 crossref_primary_10_1016_j_est_2025_115440 crossref_primary_10_1016_j_energy_2025_138545 crossref_primary_10_1016_j_scitotenv_2024_177675 crossref_primary_10_3390_en17184702 crossref_primary_10_1016_j_enconman_2024_118799 crossref_primary_10_1016_j_enconman_2024_118953 crossref_primary_10_1016_j_energy_2025_137938 crossref_primary_10_1016_j_rser_2025_115896 crossref_primary_10_1016_j_apenergy_2024_124655 crossref_primary_10_1016_j_energy_2025_138284 crossref_primary_10_1016_j_rser_2025_116080 crossref_primary_10_1016_j_apenergy_2025_126669 crossref_primary_10_1016_j_jobe_2025_113244 crossref_primary_10_1007_s10668_025_06615_4 crossref_primary_10_1016_j_enconman_2025_120018 crossref_primary_10_1016_j_energy_2024_134050
Cites_doi	10.1016/j.oceaneng.2023.113824 10.1016/j.eswa.2015.10.039 10.1016/j.renene.2019.08.094 10.1016/j.advengsoft.2013.12.007 10.1109/MCS.2014.2333253 10.1016/S0045-7825(01)00323-1 10.1016/j.engappai.2021.104448 10.1016/j.apenergy.2023.121397 10.1016/j.marstruc.2016.06.005 10.1016/j.swevo.2014.10.005 10.1016/j.apenergy.2020.114998 10.1115/OMAE2008-57056 10.1145/3300148 10.1016/S0167-6105(02)00260-X 10.1016/j.rser.2021.111625 10.1016/j.energy.2023.129619 10.1016/j.apenergy.2022.120388 10.1016/j.apenergy.2023.122186 10.1016/j.asoc.2018.07.022 10.1007/s10489-016-0825-8 10.1016/j.energy.2016.07.069 10.36688/ewtec-2023-590 10.1016/j.energy.2022.124623 10.1109/TEVC.2007.892759 10.1016/j.rser.2023.113428 10.1162/106365600568167 10.1016/j.energy.2023.129109 10.1016/j.joule.2020.09.002 10.1016/j.apenergy.2023.121890 10.1016/j.rser.2012.02.071 10.1007/s11802-023-5242-0 10.1007/s00521-015-1920-1 10.1145/3512290.3528847 10.1088/2516-1083/ac821d 10.1016/j.energy.2023.129148 10.1145/3453474 10.1016/j.apenergy.2022.120389 10.1016/j.apenergy.2021.117257 10.3390/jmse8070508 10.1109/TEVC.2004.826067 10.1016/j.enconman.2020.113324 10.1016/j.apenergy.2023.121512 10.1016/j.apenergy.2022.119713 10.1016/j.neucom.2016.10.014 10.1162/evco.1994.2.3.221 10.1016/j.compeleceng.2017.01.025 10.1016/j.apenergy.2022.120227 10.1016/j.apenergy.2023.121410 10.1016/j.rser.2009.11.003 10.1016/j.energy.2020.119107 10.1016/j.apor.2019.102023 10.1016/j.apor.2017.10.004 10.1016/j.ins.2020.03.112 10.1016/j.renene.2022.05.164 10.1016/j.apenergy.2022.120012 10.1016/j.oceaneng.2022.112033 10.1007/s00521-022-07704-5 10.1016/j.energy.2022.125617 10.1109/4235.996017 10.1109/TEVC.2013.2281535 10.1002/wrcr.20224 10.1016/j.eswa.2019.113134 10.3390/en15228681
ContentType	Journal Article
Copyright	2024 The Author(s)
Copyright_xml	– notice: 2024 The Author(s)
DBID	6I. AAFTH AAYXX CITATION 7S9 L.6 ABGEM ADTPV AOWAS D8T DF7 ZZAVC
DOI	10.1016/j.apenergy.2024.122955
DatabaseName	ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef AGRICOLA AGRICOLA - Academic SWEPUB Mälardalens högskola full text SwePub SwePub Articles SWEPUB Freely available online SWEPUB Mälardalens högskola SwePub Articles full text
DatabaseTitle	CrossRef AGRICOLA AGRICOLA - Academic
DatabaseTitleList	AGRICOLA
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Environmental Sciences
EISSN	1872-9118
ExternalDocumentID	oai_DiVA_org_mdh_66338 10_1016_j_apenergy_2024_122955 S0306261924003386
GeographicLocations	Australia
GeographicLocations_xml	– name: Australia
GroupedDBID	--K --M .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 6I. 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAFTH AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARJD AAXUO ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJOXV AKRWK ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AXJTR BELTK BJAXD BKOJK BLXMC CS3 EBS EFJIC EO8 EO9 EP2 EP3 FDB FIRID FNPLU FYGXN G-Q GBLVA IHE J1W JARJE JJJVA KOM M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 RIG ROL RPZ SDF SDG SES SEW SPC SPCBC SSR SST SSZ T5K TN5 ~02 ~G- 9DU AAHBH AAQXK AATTM AAXKI AAYWO AAYXX ABEFU ABFNM ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EFLBG EJD FEDTE FGOYB G-2 HVGLF HZ~ LY6 R2- SAC WUQ ZY4 ~HD 7S9 L.6 ABGEM ADTPV AOWAS D8T DF7 ZZAVC
ID	FETCH-LOGICAL-c430t-c696c2984e6203ad3ee816de987de835e26414b3009c6665f09c2cc0b845a33d3
ISICitedReferencesCount	27
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001219088400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0306-2619 1872-9118
IngestDate	Thu Oct 30 11:29:24 EDT 2025 Thu Oct 02 21:43:44 EDT 2025 Tue Nov 18 22:18:54 EST 2025 Sat Nov 29 07:22:28 EST 2025 Sat Apr 13 16:37:53 EDT 2024
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Keywords	Swarm-intelligence algorithms Offshore wind turbine Hybrid wave-wind energy systems Sustainable energy Wave energy converters Multi-objective optimisation algorithm Genetic algorithms
Language	English
License	This is an open access article under the CC BY license.
LinkModel	OpenURL
MergedId	FETCHMERGED-LOGICAL-c430t-c696c2984e6203ad3ee816de987de835e26414b3009c6665f09c2cc0b845a33d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ORCID	0000-0001-9002-3203 0000-0002-3418-398X 0000-0002-9537-9513
OpenAccessLink	https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-66338
PQID	3153787957
PQPubID	24069
ParticipantIDs	swepub_primary_oai_DiVA_org_mdh_66338 proquest_miscellaneous_3153787957 crossref_primary_10_1016_j_apenergy_2024_122955 crossref_citationtrail_10_1016_j_apenergy_2024_122955 elsevier_sciencedirect_doi_10_1016_j_apenergy_2024_122955
PublicationCentury	2000
PublicationDate	2024-05-15
PublicationDateYYYYMMDD	2024-05-15
PublicationDate_xml	– month: 05 year: 2024 text: 2024-05-15 day: 15
PublicationDecade	2020
PublicationTitle	Applied energy
PublicationYear	2024
Publisher	Elsevier Ltd
Publisher_xml	– name: Elsevier Ltd
References	Tian, Si, Zhang, Cheng, He, Tan (b95) 2021; 54 Hu, Yang, Sun, Wei, Zhao (b70) 2017; 222 Shadmani, Nikoo, Etri, Gandomi (b71) 2023; 347 Mirjalili, Saremi, Mirjalili, Coelho (b31) 2016; 47 Østergaard, Duic, Noorollahi, Mikulcic, Kalogirou (b11) 2020; 146 Mirjalili (b47) 2016; 27 Chen, Xiao, Zhou, Li, Zhang (b40) 2022; 10 Mirjalili, Mirjalili, Lewis (b26) 2014; 69 Srinivas, Deb (b78) 1994; 2 Letcher (b32) 2008; 30 Corne, Knowles, Oates (b44) 2000 Prina, Groppi, Nastasi, Garcia (b6) 2021; 152 Luan (b51) 2018 Babarit, Hals, Kurniawan, Muliawan, Moan, Krokstad (b53) 2011 Deng, Zhang, Miao, Zhao, Wang, Zhang (b19) 2023; 22 Deb, Pratap, Agarwal, Meyarivan (b24) 2002; 6 Jonkman JM, Hayman G, Jonkman B, Damiani R, Murray R. AeroDyn v15 user’s guide and theory manual. NREL Draft Report, 2015, p. 46. Mirjalili, Jangir, Saremi (b46) 2017; 46 Coello, Lechuga (b97) 2002; Vol. 2 Liu, Yang, Li (b84) 2020; 145 Zhang, Li (b48) 2007; 11 (b64) 2019 Guzović, Duić, Piacentino, Markovska, Mathiesen, Lund (b93) 2023; 263 Zou, Gong, Ouyang (b94) 2023; 351 Celesti ML, Paduano B, Peña-Sanchez Y, Pasta E, Faedo N, Ringwood J. Design considerations for a hybrid wind-wave platform under energy-maximising control. In: Proceedings of the European wave and tidal energy conference. Vol. 15, 2023. Amini, Mehdipour, Faraggiana, Golbaz, Mozaffari, Bracco (b3) 2022; 194 Burton, Sharpe, Jenkins, Bossanyi (b58) 2001 Mirjalili, Lewis (b92) 2015; 21 Lee (b54) 1995 Van Der Tempel, Diepeveen, De Vries, Salzmann (b59) 2011 Jonkman, Butterfield, Musial, Scott (b52) 2009 Coello, Lamont, Van Veldhuizen (b49) 2007 Pulido, Coello Coello (b98) 2004 Guerreiro, Fonseca, Paquete (b91) 2021; 54 Zitzler, Laumanns, Thiele (b45) 2001; 103 Sclavounos P, Tracy C, Lee S. Floating offshore wind turbines: Responses in a seastate Pareto optimal designs and economic assessment. In: International conference on offshore mechanics and arctic engineering. Vol. 48234, 2008, p. 31–41. Pfeifer, Guzović, Piacentino, Markovska, Duić, Lund (b12) 2023; 284 Nastasi, Markovska, Puksec, Duić, Foley (b21) 2023; 182 de Faria, de Queiroz, DeCarolis (b4) 2022; 326 Li, Yao (b68) 2019; 52 Neshat, Nezhad, Sergiienko, Mirjalili, Piras, Garcia (b23) 2022; 256 Neshat, Alexander, Wagner (b85) 2020; 534 (b67) 1991 Kemp, McCowage, Wang (b13) 2021 Ringwood, Bacelli, Fusco (b28) 2014; 34 Zhang, Li, Hu, Deng, Xiao, Chen (b42) 2022; 15 (b63) 2019 Coello, Pulido, Lechuga (b88) 2004; 8 Liu, Zhai, Hu (b77) 2023; 348 Liu, Hu, Gao, Chen, Zeng, Wei (b37) 2021; 215 Fadaee, Radzi (b25) 2012; 16 Dong, Li, Li, Cao, Jiang, Shi (b7) 2022; 4 Coello, Lamont, Van Veldhuizen (b30) 2007 Cipolletta, Crivellari, Moreno, Cozzani (b5) 2023; 347 Morteza, Sadipour, Fard, Taheri, Ahmadi (b36) 2022 Zhang, Li (b43) 2007; 11 Liu, Li, Duan, Chen (b76) 2020; 224 Zheng W, Doerr B. Better approximation guarantees for the NSGA-II by using the current crowding distance. In: Proceedings of the genetic and evolutionary computation conference. 2022, p. 611–9. Zhang, Yang, Zhao, Han, Zhou, Zhang (b16) 2024; 287 Guzovi, Duić, Piacentino, Markovska, Mathiesen, Lund (b15) 2023; 284 Robertson, Jonkman, Masciola, Song, Goupee, Coulling (b18) 2014 Teegavarapu, Ferreira, Simonovic (b39) 2013; 49 Coello (b73) 2018 Deb, Jain (b79) 2013; 18 Michailides, Gao, Moan (b27) 2016; 50 Medhane, Sangaiah (b75) 2017; 58 Chen, Huang, Shahabi (b82) 2021; 298 Qian, Sun, Zhong, Zeng, Xu, Zhou (b74) 2024; 355 Karimirad (b2) 2014 Astariz, Iglesias (b34) 2016; 113 Tian, Wang, Shi, Michailides, Wan, Chen (b41) 2023; 272 Niveditha, Singaravel (b80) 2022; 324 Kluger, Haji, Slocum (b22) 2023; 331 da Silva, de Oliveira, Cazzolato, Sergiienko, Amaral, Ding (b60) 2022; 261 Holmes, Bekele (b61) 2001 Hu, Zhou, Vogel, Liu, Willden, Sun (b20) 2020; 269 Gao, Ertugrul, Ding, Negnevitsky (b17) 2020 Wang, Du, Lu, Yang, Niu (b100) 2018; 72 Cheng, Luo, Jenkins, Larson (b10) 2023; 331 Coello, Pulido, Lechuga (b38) 2004; 8 Malan, Ochoa (b90) 2021 Korde, Lyu, Robinett III, Wilson, Bacelli, Abdelkhalik (b35) 2017; 69 da Silva, Sergiienko, Pesce, Ding, Cazzolato, Morishita (b65) 2020; 95 Zitzler, Thiele (b96) 1998; 43 Luan, Michailides, Gao, Moan (b50) 2014; Vol. 45547 Zou, Gong, Ouyang (b81) 2023; 329 Deutch (b9) 2020; 4 Deb, Agrawal, Pratap, Meyarivan (b86) 2000 Knowles, Corne (b89) 2000; 8 Falcão (b33) 2010; 14 on Waves TSC. Final Report and Recommendations to the 23rd ITTC. In: Proceedings of the 23rd international towing tank conference. Vol. II, 2002, p. 505–736. Sø rensen, Hansen, Rosas (b62) 2002; 90 Pfeifer, Feijoo, Duić (b1) 2023; 284 Tian, Yuan, Li (b101) 2021; 106 Deb (b72) 2011 Makhadmeh, Alomari, Mirjalili, Al-Betar, Elnagar (b83) 2022; 34 Trinh, Pattiaratchi, Bui (b8) 2020; 8 Coello (b69) 2002; 191 da Silva, Cazzolato, Sergiienko, Ding (b55) 2021 Lalwani, Singhal, Kumar, Gupta (b99) 2013; 2 Adams (b14) 2021 Li (10.1016/j.apenergy.2024.122955_b68) 2019; 52 Mirjalili (10.1016/j.apenergy.2024.122955_b26) 2014; 69 Coello (10.1016/j.apenergy.2024.122955_b38) 2004; 8 Michailides (10.1016/j.apenergy.2024.122955_b27) 2016; 50 Van Der Tempel (10.1016/j.apenergy.2024.122955_b59) 2011 Astariz (10.1016/j.apenergy.2024.122955_b34) 2016; 113 Guzović (10.1016/j.apenergy.2024.122955_b93) 2023; 263 Lalwani (10.1016/j.apenergy.2024.122955_b99) 2013; 2 Luan (10.1016/j.apenergy.2024.122955_b50) 2014; Vol. 45547 Medhane (10.1016/j.apenergy.2024.122955_b75) 2017; 58 Mirjalili (10.1016/j.apenergy.2024.122955_b31) 2016; 47 Liu (10.1016/j.apenergy.2024.122955_b84) 2020; 145 Zhang (10.1016/j.apenergy.2024.122955_b43) 2007; 11 10.1016/j.apenergy.2024.122955_b29 Chen (10.1016/j.apenergy.2024.122955_b40) 2022; 10 Coello (10.1016/j.apenergy.2024.122955_b97) 2002; Vol. 2 Makhadmeh (10.1016/j.apenergy.2024.122955_b83) 2022; 34 Morteza (10.1016/j.apenergy.2024.122955_b36) 2022 Liu (10.1016/j.apenergy.2024.122955_b76) 2020; 224 Cipolletta (10.1016/j.apenergy.2024.122955_b5) 2023; 347 Neshat (10.1016/j.apenergy.2024.122955_b23) 2022; 256 Østergaard (10.1016/j.apenergy.2024.122955_b11) 2020; 146 Deb (10.1016/j.apenergy.2024.122955_b72) 2011 10.1016/j.apenergy.2024.122955_b56 Mirjalili (10.1016/j.apenergy.2024.122955_b92) 2015; 21 Sø rensen (10.1016/j.apenergy.2024.122955_b62) 2002; 90 Hu (10.1016/j.apenergy.2024.122955_b70) 2017; 222 10.1016/j.apenergy.2024.122955_b57 Dong (10.1016/j.apenergy.2024.122955_b7) 2022; 4 Teegavarapu (10.1016/j.apenergy.2024.122955_b39) 2013; 49 Coello (10.1016/j.apenergy.2024.122955_b88) 2004; 8 Karimirad (10.1016/j.apenergy.2024.122955_b2) 2014 Knowles (10.1016/j.apenergy.2024.122955_b89) 2000; 8 Holmes (10.1016/j.apenergy.2024.122955_b61) 2001 da Silva (10.1016/j.apenergy.2024.122955_b60) 2022; 261 Guerreiro (10.1016/j.apenergy.2024.122955_b91) 2021; 54 Zhang (10.1016/j.apenergy.2024.122955_b16) 2024; 287 Shadmani (10.1016/j.apenergy.2024.122955_b71) 2023; 347 Zou (10.1016/j.apenergy.2024.122955_b94) 2023; 351 Tian (10.1016/j.apenergy.2024.122955_b95) 2021; 54 Niveditha (10.1016/j.apenergy.2024.122955_b80) 2022; 324 Adams (10.1016/j.apenergy.2024.122955_b14) 2021 Deutch (10.1016/j.apenergy.2024.122955_b9) 2020; 4 Trinh (10.1016/j.apenergy.2024.122955_b8) 2020; 8 Gao (10.1016/j.apenergy.2024.122955_b17) 2020 Liu (10.1016/j.apenergy.2024.122955_b37) 2021; 215 Burton (10.1016/j.apenergy.2024.122955_b58) 2001 Deb (10.1016/j.apenergy.2024.122955_b86) 2000 Jonkman (10.1016/j.apenergy.2024.122955_b52) 2009 Tian (10.1016/j.apenergy.2024.122955_b101) 2021; 106 Coello (10.1016/j.apenergy.2024.122955_b49) 2007 Ringwood (10.1016/j.apenergy.2024.122955_b28) 2014; 34 Qian (10.1016/j.apenergy.2024.122955_b74) 2024; 355 de Faria (10.1016/j.apenergy.2024.122955_b4) 2022; 326 Mirjalili (10.1016/j.apenergy.2024.122955_b47) 2016; 27 Neshat (10.1016/j.apenergy.2024.122955_b85) 2020; 534 Pfeifer (10.1016/j.apenergy.2024.122955_b1) 2023; 284 Coello (10.1016/j.apenergy.2024.122955_b73) 2018 Liu (10.1016/j.apenergy.2024.122955_b77) 2023; 348 10.1016/j.apenergy.2024.122955_b66 Tian (10.1016/j.apenergy.2024.122955_b41) 2023; 272 Robertson (10.1016/j.apenergy.2024.122955_b18) 2014 da Silva (10.1016/j.apenergy.2024.122955_b65) 2020; 95 Pulido (10.1016/j.apenergy.2024.122955_b98) 2004 Deb (10.1016/j.apenergy.2024.122955_b79) 2013; 18 Deb (10.1016/j.apenergy.2024.122955_b24) 2002; 6 Chen (10.1016/j.apenergy.2024.122955_b82) 2021; 298 Coello (10.1016/j.apenergy.2024.122955_b30) 2007 Deng (10.1016/j.apenergy.2024.122955_b19) 2023; 22 Srinivas (10.1016/j.apenergy.2024.122955_b78) 1994; 2 Wang (10.1016/j.apenergy.2024.122955_b100) 2018; 72 Hu (10.1016/j.apenergy.2024.122955_b20) 2020; 269 Zitzler (10.1016/j.apenergy.2024.122955_b45) 2001; 103 Babarit (10.1016/j.apenergy.2024.122955_b53) 2011 Coello (10.1016/j.apenergy.2024.122955_b69) 2002; 191 Korde (10.1016/j.apenergy.2024.122955_b35) 2017; 69 Luan (10.1016/j.apenergy.2024.122955_b51) 2018 Fadaee (10.1016/j.apenergy.2024.122955_b25) 2012; 16 Zitzler (10.1016/j.apenergy.2024.122955_b96) 1998; 43 Zou (10.1016/j.apenergy.2024.122955_b81) 2023; 329 Cheng (10.1016/j.apenergy.2024.122955_b10) 2023; 331 (10.1016/j.apenergy.2024.122955_b64) 2019 Kemp (10.1016/j.apenergy.2024.122955_b13) 2021 Zhang (10.1016/j.apenergy.2024.122955_b42) 2022; 15 Corne (10.1016/j.apenergy.2024.122955_b44) 2000 Zhang (10.1016/j.apenergy.2024.122955_b48) 2007; 11 Falcão (10.1016/j.apenergy.2024.122955_b33) 2010; 14 Nastasi (10.1016/j.apenergy.2024.122955_b21) 2023; 182 Letcher (10.1016/j.apenergy.2024.122955_b32) 2008; 30 da Silva (10.1016/j.apenergy.2024.122955_b55) 2021 Prina (10.1016/j.apenergy.2024.122955_b6) 2021; 152 10.1016/j.apenergy.2024.122955_b87 (10.1016/j.apenergy.2024.122955_b67) 1991 Malan (10.1016/j.apenergy.2024.122955_b90) 2021 Guzovi (10.1016/j.apenergy.2024.122955_b15) 2023; 284 Pfeifer (10.1016/j.apenergy.2024.122955_b12) 2023; 284 (10.1016/j.apenergy.2024.122955_b63) 2019 Mirjalili (10.1016/j.apenergy.2024.122955_b46) 2017; 46 Lee (10.1016/j.apenergy.2024.122955_b54) 1995 Kluger (10.1016/j.apenergy.2024.122955_b22) 2023; 331 Amini (10.1016/j.apenergy.2024.122955_b3) 2022; 194
References_xml	– volume: 58 start-page: 126 year: 2017 end-page: 143 ident: b75 article-title: Search space-based multi-objective optimization evolutionary algorithm publication-title: Comput Electr Eng – year: 2007 ident: b49 article-title: Evolutionary algorithms for solving multi-objective problems – volume: 324 year: 2022 ident: b80 article-title: Optimal sizing of hybrid PV–wind–battery storage system for net zero energy buildings to reduce grid burden publication-title: Appl Energy – volume: 54 start-page: 1 year: 2021 end-page: 42 ident: b91 article-title: The hypervolume indicator: Computational problems and algorithms publication-title: ACM Comput Surv – reference: Zheng W, Doerr B. Better approximation guarantees for the NSGA-II by using the current crowding distance. In: Proceedings of the genetic and evolutionary computation conference. 2022, p. 611–9. – volume: 222 start-page: 170 year: 2017 end-page: 182 ident: b70 article-title: An improved multi-objective evolutionary algorithm based on environmental and history information publication-title: Neurocomputing – volume: 54 start-page: 1 year: 2021 end-page: 34 ident: b95 article-title: Evolutionary large-scale multi-objective optimization: A survey publication-title: ACM Comput Surv – year: 1991 ident: b67 article-title: Wind turbine generator systems-part 1: Safety requirements – volume: 284 start-page: 1 year: 2023 end-page: 20 ident: b15 article-title: SDEWES science-The path to a sustainable carbon neutral world publication-title: Energy – year: 2014 ident: b18 article-title: Definition of the semisubmersible floating system for phase II of OC4 – volume: 347 year: 2023 ident: b71 article-title: A multi-objective approach for location and layout optimization of wave energy converters publication-title: Appl Energy – year: 2019 ident: b64 article-title: Marine energy - Wave, tidal and other water current converters - Part 2: Marine energy systems - Design requirements – volume: 261 year: 2022 ident: b60 article-title: Statistical linearisation of a nonlinear floating offshore wind turbine under random waves and winds publication-title: Ocean Eng – volume: 146 start-page: 2430 year: 2020 end-page: 2437 ident: b11 article-title: Sustainable development using renewable energy technology publication-title: Renew Energy – start-page: 1 year: 2011 end-page: 317 ident: b53 article-title: The NumWEC project publication-title: Numerical estimation of energy delivery from a selection of wave energy converters–Final report – start-page: 1 year: 2020 end-page: 6 ident: b17 article-title: Offshore wind, wave and integrated energy conversion systems: A review and future publication-title: 2020 Australasian universities power engineering conference – volume: 2 start-page: 221 year: 1994 end-page: 248 ident: b78 article-title: Muiltiobjective optimization using nondominated sorting in genetic algorithms publication-title: Evol Comput – volume: 298 year: 2021 ident: b82 article-title: Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm publication-title: Appl Energy – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b48 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans Evol Comput – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b24 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans Evol Comput – volume: 10 year: 2022 ident: b40 article-title: Fully coupled analysis of an integrated floating wind-wave power generation platform in operational sea-states publication-title: Front Energy Res – volume: 284 year: 2023 ident: b1 article-title: Fast energy transition as a best strategy for all? The nash equilibrium of long-term energy planning strategies in coupled power markets publication-title: Energy – volume: 329 year: 2023 ident: b81 article-title: A non-dominated sorting genetic approach using elite crossover for the combined cooling, heating, and power system with three energy storages publication-title: Appl Energy – volume: 30 start-page: 20 year: 2008 end-page: 21 ident: b32 article-title: Future energy: Improved, sustainable, and clean options for our planet publication-title: Chem Int–Newsmagazine IUPAC – year: 1995 ident: b54 article-title: WAMIT theory manual – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b43 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans Evol Comput – volume: 269 year: 2020 ident: b20 article-title: Optimal design and performance analysis of a hybrid system combing a floating wind platform and wave energy converters publication-title: Appl Energy – volume: 263 year: 2023 ident: b93 article-title: Paving the way for the Paris agreement: Contributions of SDEWES science publication-title: Energy – volume: 272 year: 2023 ident: b41 article-title: Numerical study of hydrodynamic responses for a combined concept of semisubmersible wind turbine and different layouts of a wave energy converter publication-title: Ocean Eng – volume: 331 year: 2023 ident: b10 article-title: The value of low-and negative-carbon fuels in the transition to net-zero emission economies: Lifecycle greenhouse gas emissions and cost assessments across multiple fuel types publication-title: Appl Energy – volume: 284 year: 2023 ident: b12 article-title: Cutting-edge science for sustainable development-SDEWES 2022 special issue publication-title: Energy – year: 2014 ident: b2 article-title: Offshore energy structures: for wind power, wave energy and hybrid marine platforms – volume: 326 year: 2022 ident: b4 article-title: Optimizing offshore renewable portfolios under resource variability publication-title: Appl Energy – volume: 113 start-page: 497 year: 2016 end-page: 508 ident: b34 article-title: Co-located wind and wave energy farms: Uniformly distributed arrays publication-title: Energy – volume: 4 start-page: 2237 year: 2020 end-page: 2240 ident: b9 article-title: Is net zero carbon 2050 possible? publication-title: Joule – volume: 191 start-page: 1245 year: 2002 end-page: 1287 ident: b69 article-title: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art publication-title: Comput Methods Appl Mech Engrg – volume: 15 start-page: 8681 year: 2022 ident: b42 article-title: Research on size optimization of wave energy converters based on a floating wind-wave combined power generation platform publication-title: Energies – year: 2022 ident: b36 article-title: A dagging-based deep learning framework for transmission line flexibility assessment publication-title: IET Renew Power Gener – volume: 69 start-page: 46 year: 2014 end-page: 61 ident: b26 article-title: Grey wolf optimizer publication-title: Adv Eng Softw – volume: 348 year: 2023 ident: b77 article-title: Multi-objective optimization with advanced exergy analysis of a wind-solar-hydrogen multi-energy supply system publication-title: Appl Energy – volume: 103 year: 2001 ident: b45 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm publication-title: TIK-Rep – volume: 351 year: 2023 ident: b94 article-title: The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant publication-title: Appl Energy – volume: 49 start-page: 3180 year: 2013 end-page: 3193 ident: b39 article-title: Fuzzy multiobjective models for optimal operation of a hydropower system publication-title: Water Resour Res – year: 2009 ident: b52 article-title: Definition of a 5-MW reference wind turbine for offshore system development – start-page: 849 year: 2000 end-page: 858 ident: b86 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II publication-title: Parallel problem solving from nature PPSN VI: 6th international conference Paris, France, September 18–20, 2000 proceedings. Vol. 6 – volume: 43 year: 1998 ident: b96 article-title: An evolutionary algorithm for multiobjective optimization: The strength pareto approach publication-title: TIK-Rep – volume: 331 year: 2023 ident: b22 article-title: The power balancing benefits of wave energy converters in offshore wind-wave farms with energy storage publication-title: Appl Energy – volume: 95 year: 2020 ident: b65 article-title: Stochastic analysis of nonlinear wave energy converters via statistical linearization publication-title: Appl Ocean Res – volume: 90 start-page: 1381 year: 2002 end-page: 1402 ident: b62 article-title: Wind models for simulation of power fluctuations from wind farms publication-title: J Wind Eng Ind Aerodyn – volume: 27 start-page: 1053 year: 2016 end-page: 1073 ident: b47 article-title: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems publication-title: Neural Comput Appl – volume: 72 start-page: 321 year: 2018 end-page: 337 ident: b100 article-title: An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting publication-title: Appl Soft Comput – start-page: 1 year: 2021 end-page: 17 ident: b55 article-title: Efficient estimation of the nonlinear aerodynamic loads of floating offshore wind turbines under random waves and wind in frequency domain publication-title: J Ocean Eng Mar Energy – reference: Sclavounos P, Tracy C, Lee S. Floating offshore wind turbines: Responses in a seastate Pareto optimal designs and economic assessment. In: International conference on offshore mechanics and arctic engineering. Vol. 48234, 2008, p. 31–41. – volume: 2 start-page: 39 year: 2013 end-page: 101 ident: b99 article-title: A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm publication-title: Trans Combinat – volume: 16 start-page: 3364 year: 2012 end-page: 3369 ident: b25 article-title: Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review publication-title: Renew Sustain Energy Rev – volume: 256 year: 2022 ident: b23 article-title: Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser publication-title: Energy – volume: 534 start-page: 218 year: 2020 end-page: 244 ident: b85 article-title: A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters publication-title: Inform Sci – volume: 8 start-page: 256 year: 2004 end-page: 279 ident: b88 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans Evol Comput – volume: 21 start-page: 1 year: 2015 end-page: 23 ident: b92 article-title: Novel performance metrics for robust multi-objective optimization algorithms publication-title: Swarm Evol Comput – year: 2011 ident: b72 article-title: Multi-objective optimisation using evolutionary algorithms: an introduction – volume: 224 year: 2020 ident: b76 article-title: A review on multi-objective optimization framework in wind energy forecasting techniques and applications publication-title: Energy Convers Manage – volume: 8 start-page: 256 year: 2004 end-page: 279 ident: b38 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans Evol Comput – volume: 106 year: 2021 ident: b101 article-title: Robust optimization of the continuous annealing process based on a novel Multi-Objective Dragonfly Algorithm publication-title: Eng Appl Artif Intell – start-page: 899 year: 2021 end-page: 917 ident: b90 article-title: Recent advances in landscape analysis for optimisation and learning publication-title: GECCO companion – year: 2021 ident: b13 article-title: Towards net zero: implications for Australia of energy policies in East Asia — Bulletin – September 2021 – volume: 18 start-page: 577 year: 2013 end-page: 601 ident: b79 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans Evol Comput – start-page: 3 year: 2021 ident: b14 article-title: Zero greenhouse gas emissions by 2050: What it means for the Australian economy, industries and regions – volume: 347 year: 2023 ident: b5 article-title: Design of sustainable offshore hybrid energy systems for improved wave energy dispatchability publication-title: Appl Energy – volume: 34 start-page: 19723 year: 2022 end-page: 19749 ident: b83 article-title: Recent advances in multi-objective grey wolf optimizer, its versions and applications publication-title: Neural Comput Appl – start-page: 225 year: 2004 end-page: 237 ident: b98 article-title: Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer publication-title: Genetic and evolutionary computation conference – volume: 47 start-page: 106 year: 2016 end-page: 119 ident: b31 article-title: Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization publication-title: Expert Syst Appl – volume: 287 year: 2024 ident: b16 article-title: A novel wake control strategy for a twin-rotor floating wind turbine: Mitigating wake effect publication-title: Energy – reference: Celesti ML, Paduano B, Peña-Sanchez Y, Pasta E, Faedo N, Ringwood J. Design considerations for a hybrid wind-wave platform under energy-maximising control. In: Proceedings of the European wave and tidal energy conference. Vol. 15, 2023. – year: 2019 ident: b63 article-title: Wind energy generation systems - Part 3-2: Design requirements for floating offshore wind turbines – volume: 355 year: 2024 ident: b74 article-title: Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm publication-title: Appl Energy – volume: 34 start-page: 30 year: 2014 end-page: 55 ident: b28 article-title: Energy-maximizing control of wave-energy converters: The development of control system technology to optimize their operation publication-title: IEEE Control Syst Mag – volume: 152 year: 2021 ident: b6 article-title: Bottom-up energy system models applied to sustainable islands publication-title: Renew Sustain Energy Rev – volume: 22 start-page: 1165 year: 2023 end-page: 1180 ident: b19 article-title: Multi-objective optimal design of the wind-wave hybrid platform with the coupling interaction publication-title: J Ocean Univ China – start-page: 463 year: 2011 end-page: 478 ident: b59 article-title: Offshore environmental loads and wind turbine design: impact of wind, wave, currents and ice publication-title: Wind energy systems – volume: 182 year: 2023 ident: b21 article-title: Techniques and technologies to board on the feasible renewable and sustainable energy systems publication-title: Renew Sustain Energy Rev – year: 2001 ident: b61 article-title: Wind loading of structures – volume: 215 year: 2021 ident: b37 article-title: A high-precise model for the hydraulic power take-off of a raft-type wave energy converter publication-title: Energy – start-page: 177 year: 2018 end-page: 204 ident: b73 article-title: Multi-objective optimization publication-title: Handbook of heuristics – year: 2007 ident: b30 article-title: Evolutionary algorithms for solving multi-objective problems – volume: 50 start-page: 35 year: 2016 end-page: 54 ident: b27 article-title: Experimental and numerical study of the response of the offshore combined wind/wave energy concept SFC in extreme environmental conditions publication-title: Mar Struct – year: 2001 ident: b58 article-title: Wind energy handbook – reference: Jonkman JM, Hayman G, Jonkman B, Damiani R, Murray R. AeroDyn v15 user’s guide and theory manual. NREL Draft Report, 2015, p. 46. – volume: 8 start-page: 149 year: 2000 end-page: 172 ident: b89 article-title: Approximating the nondominated front using the Pareto archived evolution strategy publication-title: Evol Comput – volume: Vol. 2 start-page: 1051 year: 2002 end-page: 1056 ident: b97 article-title: MOPSO: A proposal for multiple objective particle swarm optimization publication-title: Proceedings of the 2002 congress on evolutionary computation – volume: 145 year: 2020 ident: b84 article-title: A multiple search strategies based grey wolf optimizer for solving multi-objective optimization problems publication-title: Expert Syst Appl – volume: Vol. 45547 year: 2014 ident: b50 article-title: Modeling and analysis of a 5 MW semi-submersible wind turbine combined with three flap-type Wave Energy Converters publication-title: International conference on offshore mechanics and arctic engineering – volume: 4 year: 2022 ident: b7 article-title: A state-of-the-art review of the hybrid wind-wave energy converter publication-title: Progr Energy – reference: on Waves TSC. Final Report and Recommendations to the 23rd ITTC. In: Proceedings of the 23rd international towing tank conference. Vol. II, 2002, p. 505–736. – volume: 8 start-page: 508 year: 2020 ident: b8 article-title: The contribution of forerunner to storm surges along the Vietnam Coast publication-title: J Mar Sci Eng – start-page: 839 year: 2000 end-page: 848 ident: b44 article-title: The Pareto envelope-based selection algorithm for multiobjective optimization publication-title: Parallel problem solving from nature PPSN VI: 6th international conference Paris, France, September 18–20, 2000 proceedings. Vol. 6 – volume: 46 start-page: 79 year: 2017 end-page: 95 ident: b46 article-title: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems publication-title: Appl Intell – volume: 52 start-page: 1 year: 2019 end-page: 38 ident: b68 article-title: Quality evaluation of solution sets in multiobjective optimisation: A survey publication-title: ACM Comput Surv – volume: 14 start-page: 899 year: 2010 end-page: 918 ident: b33 article-title: Wave energy utilization: A review of the technologies publication-title: Renew Sustain Energy Rev – volume: 194 start-page: 938 year: 2022 end-page: 954 ident: b3 article-title: Optimization of hydraulic power take-off system settings for point absorber wave energy converter publication-title: Renew Energy – year: 2018 ident: b51 article-title: Design and analysis for a steel braceless semi-submersible hull for supporting a 5-MW horizontal axis wind turbine – volume: 69 start-page: 126 year: 2017 end-page: 137 ident: b35 article-title: Constrained near-optimal control of a wave energy converter in three oscillation modes publication-title: Appl Ocean Res – volume: 272 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b41 article-title: Numerical study of hydrodynamic responses for a combined concept of semisubmersible wind turbine and different layouts of a wave energy converter publication-title: Ocean Eng doi: 10.1016/j.oceaneng.2023.113824 – volume: 47 start-page: 106 year: 2016 ident: 10.1016/j.apenergy.2024.122955_b31 article-title: Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.10.039 – volume: 146 start-page: 2430 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b11 article-title: Sustainable development using renewable energy technology publication-title: Renew Energy doi: 10.1016/j.renene.2019.08.094 – volume: 69 start-page: 46 year: 2014 ident: 10.1016/j.apenergy.2024.122955_b26 article-title: Grey wolf optimizer publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2013.12.007 – year: 1991 ident: 10.1016/j.apenergy.2024.122955_b67 – volume: 34 start-page: 30 issue: 5 year: 2014 ident: 10.1016/j.apenergy.2024.122955_b28 article-title: Energy-maximizing control of wave-energy converters: The development of control system technology to optimize their operation publication-title: IEEE Control Syst Mag doi: 10.1109/MCS.2014.2333253 – volume: 30 start-page: 20 issue: 2 year: 2008 ident: 10.1016/j.apenergy.2024.122955_b32 article-title: Future energy: Improved, sustainable, and clean options for our planet publication-title: Chem Int–Newsmagazine IUPAC – volume: 191 start-page: 1245 issue: 11–12 year: 2002 ident: 10.1016/j.apenergy.2024.122955_b69 article-title: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art publication-title: Comput Methods Appl Mech Engrg doi: 10.1016/S0045-7825(01)00323-1 – volume: 106 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b101 article-title: Robust optimization of the continuous annealing process based on a novel Multi-Objective Dragonfly Algorithm publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2021.104448 – volume: 347 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b71 article-title: A multi-objective approach for location and layout optimization of wave energy converters publication-title: Appl Energy doi: 10.1016/j.apenergy.2023.121397 – volume: 50 start-page: 35 year: 2016 ident: 10.1016/j.apenergy.2024.122955_b27 article-title: Experimental and numerical study of the response of the offshore combined wind/wave energy concept SFC in extreme environmental conditions publication-title: Mar Struct doi: 10.1016/j.marstruc.2016.06.005 – start-page: 1 year: 2011 ident: 10.1016/j.apenergy.2024.122955_b53 article-title: The NumWEC project – volume: 21 start-page: 1 year: 2015 ident: 10.1016/j.apenergy.2024.122955_b92 article-title: Novel performance metrics for robust multi-objective optimization algorithms publication-title: Swarm Evol Comput doi: 10.1016/j.swevo.2014.10.005 – volume: 269 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b20 article-title: Optimal design and performance analysis of a hybrid system combing a floating wind platform and wave energy converters publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.114998 – volume: 43 year: 1998 ident: 10.1016/j.apenergy.2024.122955_b96 article-title: An evolutionary algorithm for multiobjective optimization: The strength pareto approach publication-title: TIK-Rep – year: 2007 ident: 10.1016/j.apenergy.2024.122955_b49 – ident: 10.1016/j.apenergy.2024.122955_b66 doi: 10.1115/OMAE2008-57056 – volume: 52 start-page: 1 issue: 2 year: 2019 ident: 10.1016/j.apenergy.2024.122955_b68 article-title: Quality evaluation of solution sets in multiobjective optimisation: A survey publication-title: ACM Comput Surv doi: 10.1145/3300148 – volume: 90 start-page: 1381 issue: 12–15 year: 2002 ident: 10.1016/j.apenergy.2024.122955_b62 article-title: Wind models for simulation of power fluctuations from wind farms publication-title: J Wind Eng Ind Aerodyn doi: 10.1016/S0167-6105(02)00260-X – volume: 152 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b6 article-title: Bottom-up energy system models applied to sustainable islands publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2021.111625 – volume: 287 year: 2024 ident: 10.1016/j.apenergy.2024.122955_b16 article-title: A novel wake control strategy for a twin-rotor floating wind turbine: Mitigating wake effect publication-title: Energy doi: 10.1016/j.energy.2023.129619 – volume: 284 start-page: 1 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b15 article-title: SDEWES science-The path to a sustainable carbon neutral world publication-title: Energy – volume: 331 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b10 article-title: The value of low-and negative-carbon fuels in the transition to net-zero emission economies: Lifecycle greenhouse gas emissions and cost assessments across multiple fuel types publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.120388 – volume: 355 year: 2024 ident: 10.1016/j.apenergy.2024.122955_b74 article-title: Multi-objective optimization design of the wind-to-heat system blades based on the Particle Swarm Optimization algorithm publication-title: Appl Energy doi: 10.1016/j.apenergy.2023.122186 – start-page: 1 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b55 article-title: Efficient estimation of the nonlinear aerodynamic loads of floating offshore wind turbines under random waves and wind in frequency domain publication-title: J Ocean Eng Mar Energy – volume: 72 start-page: 321 year: 2018 ident: 10.1016/j.apenergy.2024.122955_b100 article-title: An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2018.07.022 – volume: 46 start-page: 79 year: 2017 ident: 10.1016/j.apenergy.2024.122955_b46 article-title: Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems publication-title: Appl Intell doi: 10.1007/s10489-016-0825-8 – start-page: 463 year: 2011 ident: 10.1016/j.apenergy.2024.122955_b59 article-title: Offshore environmental loads and wind turbine design: impact of wind, wave, currents and ice – volume: 54 start-page: 1 issue: 8 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b95 article-title: Evolutionary large-scale multi-objective optimization: A survey publication-title: ACM Comput Surv – volume: 113 start-page: 497 year: 2016 ident: 10.1016/j.apenergy.2024.122955_b34 article-title: Co-located wind and wave energy farms: Uniformly distributed arrays publication-title: Energy doi: 10.1016/j.energy.2016.07.069 – start-page: 177 year: 2018 ident: 10.1016/j.apenergy.2024.122955_b73 article-title: Multi-objective optimization – ident: 10.1016/j.apenergy.2024.122955_b29 doi: 10.36688/ewtec-2023-590 – volume: 256 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b23 article-title: Wave power forecasting using an effective decomposition-based convolutional Bi-directional model with equilibrium Nelder-Mead optimiser publication-title: Energy doi: 10.1016/j.energy.2022.124623 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.apenergy.2024.122955_b48 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2007.892759 – ident: 10.1016/j.apenergy.2024.122955_b56 – volume: 182 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b21 article-title: Techniques and technologies to board on the feasible renewable and sustainable energy systems publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2023.113428 – volume: 8 start-page: 149 issue: 2 year: 2000 ident: 10.1016/j.apenergy.2024.122955_b89 article-title: Approximating the nondominated front using the Pareto archived evolution strategy publication-title: Evol Comput doi: 10.1162/106365600568167 – volume: 284 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b1 article-title: Fast energy transition as a best strategy for all? The nash equilibrium of long-term energy planning strategies in coupled power markets publication-title: Energy doi: 10.1016/j.energy.2023.129109 – volume: Vol. 2 start-page: 1051 year: 2002 ident: 10.1016/j.apenergy.2024.122955_b97 article-title: MOPSO: A proposal for multiple objective particle swarm optimization – volume: 2 start-page: 39 issue: 1 year: 2013 ident: 10.1016/j.apenergy.2024.122955_b99 article-title: A comprehensive survey: Applications of multi-objective particle swarm optimization (MOPSO) algorithm publication-title: Trans Combinat – year: 2014 ident: 10.1016/j.apenergy.2024.122955_b2 – volume: 4 start-page: 2237 issue: 11 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b9 article-title: Is net zero carbon 2050 possible? publication-title: Joule doi: 10.1016/j.joule.2020.09.002 – volume: 351 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b94 article-title: The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant publication-title: Appl Energy doi: 10.1016/j.apenergy.2023.121890 – volume: 16 start-page: 3364 issue: 5 year: 2012 ident: 10.1016/j.apenergy.2024.122955_b25 article-title: Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2012.02.071 – year: 2018 ident: 10.1016/j.apenergy.2024.122955_b51 – volume: 22 start-page: 1165 issue: 5 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b19 article-title: Multi-objective optimal design of the wind-wave hybrid platform with the coupling interaction publication-title: J Ocean Univ China doi: 10.1007/s11802-023-5242-0 – volume: 27 start-page: 1053 year: 2016 ident: 10.1016/j.apenergy.2024.122955_b47 article-title: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1920-1 – ident: 10.1016/j.apenergy.2024.122955_b87 doi: 10.1145/3512290.3528847 – volume: 4 issue: 4 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b7 article-title: A state-of-the-art review of the hybrid wind-wave energy converter publication-title: Progr Energy doi: 10.1088/2516-1083/ac821d – year: 1995 ident: 10.1016/j.apenergy.2024.122955_b54 – year: 2001 ident: 10.1016/j.apenergy.2024.122955_b61 – year: 2019 ident: 10.1016/j.apenergy.2024.122955_b63 – volume: Vol. 45547 year: 2014 ident: 10.1016/j.apenergy.2024.122955_b50 article-title: Modeling and analysis of a 5 MW semi-submersible wind turbine combined with three flap-type Wave Energy Converters – volume: 284 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b12 article-title: Cutting-edge science for sustainable development-SDEWES 2022 special issue publication-title: Energy doi: 10.1016/j.energy.2023.129148 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.apenergy.2024.122955_b43 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2007.892759 – volume: 54 start-page: 1 issue: 6 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b91 article-title: The hypervolume indicator: Computational problems and algorithms publication-title: ACM Comput Surv doi: 10.1145/3453474 – volume: 331 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b22 article-title: The power balancing benefits of wave energy converters in offshore wind-wave farms with energy storage publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.120389 – start-page: 839 year: 2000 ident: 10.1016/j.apenergy.2024.122955_b44 article-title: The Pareto envelope-based selection algorithm for multiobjective optimization – year: 2009 ident: 10.1016/j.apenergy.2024.122955_b52 – start-page: 849 year: 2000 ident: 10.1016/j.apenergy.2024.122955_b86 article-title: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II – volume: 298 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b82 article-title: Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm publication-title: Appl Energy doi: 10.1016/j.apenergy.2021.117257 – volume: 8 start-page: 508 issue: 7 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b8 article-title: The contribution of forerunner to storm surges along the Vietnam Coast publication-title: J Mar Sci Eng doi: 10.3390/jmse8070508 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 10.1016/j.apenergy.2024.122955_b38 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2004.826067 – year: 2007 ident: 10.1016/j.apenergy.2024.122955_b30 – start-page: 1 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b17 article-title: Offshore wind, wave and integrated energy conversion systems: A review and future – volume: 224 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b76 article-title: A review on multi-objective optimization framework in wind energy forecasting techniques and applications publication-title: Energy Convers Manage doi: 10.1016/j.enconman.2020.113324 – volume: 348 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b77 article-title: Multi-objective optimization with advanced exergy analysis of a wind-solar-hydrogen multi-energy supply system publication-title: Appl Energy doi: 10.1016/j.apenergy.2023.121512 – volume: 324 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b80 article-title: Optimal sizing of hybrid PV–wind–battery storage system for net zero energy buildings to reduce grid burden publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.119713 – year: 2001 ident: 10.1016/j.apenergy.2024.122955_b58 – year: 2014 ident: 10.1016/j.apenergy.2024.122955_b18 – volume: 222 start-page: 170 year: 2017 ident: 10.1016/j.apenergy.2024.122955_b70 article-title: An improved multi-objective evolutionary algorithm based on environmental and history information publication-title: Neurocomputing doi: 10.1016/j.neucom.2016.10.014 – volume: 2 start-page: 221 issue: 3 year: 1994 ident: 10.1016/j.apenergy.2024.122955_b78 article-title: Muiltiobjective optimization using nondominated sorting in genetic algorithms publication-title: Evol Comput doi: 10.1162/evco.1994.2.3.221 – volume: 58 start-page: 126 year: 2017 ident: 10.1016/j.apenergy.2024.122955_b75 article-title: Search space-based multi-objective optimization evolutionary algorithm publication-title: Comput Electr Eng doi: 10.1016/j.compeleceng.2017.01.025 – volume: 329 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b81 article-title: A non-dominated sorting genetic approach using elite crossover for the combined cooling, heating, and power system with three energy storages publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.120227 – volume: 347 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b5 article-title: Design of sustainable offshore hybrid energy systems for improved wave energy dispatchability publication-title: Appl Energy doi: 10.1016/j.apenergy.2023.121410 – year: 2021 ident: 10.1016/j.apenergy.2024.122955_b13 – volume: 14 start-page: 899 issue: 3 year: 2010 ident: 10.1016/j.apenergy.2024.122955_b33 article-title: Wave energy utilization: A review of the technologies publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2009.11.003 – volume: 215 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b37 article-title: A high-precise model for the hydraulic power take-off of a raft-type wave energy converter publication-title: Energy doi: 10.1016/j.energy.2020.119107 – volume: 95 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b65 article-title: Stochastic analysis of nonlinear wave energy converters via statistical linearization publication-title: Appl Ocean Res doi: 10.1016/j.apor.2019.102023 – volume: 8 start-page: 256 issue: 3 year: 2004 ident: 10.1016/j.apenergy.2024.122955_b88 article-title: Handling multiple objectives with particle swarm optimization publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2004.826067 – start-page: 899 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b90 article-title: Recent advances in landscape analysis for optimisation and learning – volume: 69 start-page: 126 year: 2017 ident: 10.1016/j.apenergy.2024.122955_b35 article-title: Constrained near-optimal control of a wave energy converter in three oscillation modes publication-title: Appl Ocean Res doi: 10.1016/j.apor.2017.10.004 – volume: 534 start-page: 218 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b85 article-title: A hybrid cooperative co-evolution algorithm framework for optimising power take off and placements of wave energy converters publication-title: Inform Sci doi: 10.1016/j.ins.2020.03.112 – volume: 194 start-page: 938 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b3 article-title: Optimization of hydraulic power take-off system settings for point absorber wave energy converter publication-title: Renew Energy doi: 10.1016/j.renene.2022.05.164 – volume: 326 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b4 article-title: Optimizing offshore renewable portfolios under resource variability publication-title: Appl Energy doi: 10.1016/j.apenergy.2022.120012 – volume: 103 year: 2001 ident: 10.1016/j.apenergy.2024.122955_b45 article-title: SPEA2: Improving the strength Pareto evolutionary algorithm publication-title: TIK-Rep – volume: 261 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b60 article-title: Statistical linearisation of a nonlinear floating offshore wind turbine under random waves and winds publication-title: Ocean Eng doi: 10.1016/j.oceaneng.2022.112033 – volume: 34 start-page: 19723 issue: 22 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b83 article-title: Recent advances in multi-objective grey wolf optimizer, its versions and applications publication-title: Neural Comput Appl doi: 10.1007/s00521-022-07704-5 – year: 2019 ident: 10.1016/j.apenergy.2024.122955_b64 – volume: 263 year: 2023 ident: 10.1016/j.apenergy.2024.122955_b93 article-title: Paving the way for the Paris agreement: Contributions of SDEWES science publication-title: Energy doi: 10.1016/j.energy.2022.125617 – year: 2011 ident: 10.1016/j.apenergy.2024.122955_b72 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.apenergy.2024.122955_b24 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.996017 – volume: 18 start-page: 577 issue: 4 year: 2013 ident: 10.1016/j.apenergy.2024.122955_b79 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2013.2281535 – start-page: 3 year: 2021 ident: 10.1016/j.apenergy.2024.122955_b14 – volume: 49 start-page: 3180 issue: 6 year: 2013 ident: 10.1016/j.apenergy.2024.122955_b39 article-title: Fuzzy multiobjective models for optimal operation of a hydropower system publication-title: Water Resour Res doi: 10.1002/wrcr.20224 – volume: 145 year: 2020 ident: 10.1016/j.apenergy.2024.122955_b84 article-title: A multiple search strategies based grey wolf optimizer for solving multi-objective optimization problems publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2019.113134 – ident: 10.1016/j.apenergy.2024.122955_b57 – volume: 10 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b40 article-title: Fully coupled analysis of an integrated floating wind-wave power generation platform in operational sea-states publication-title: Front Energy Res – year: 2022 ident: 10.1016/j.apenergy.2024.122955_b36 article-title: A dagging-based deep learning framework for transmission line flexibility assessment publication-title: IET Renew Power Gener – start-page: 225 year: 2004 ident: 10.1016/j.apenergy.2024.122955_b98 article-title: Using clustering techniques to improve the performance of a multi-objective particle swarm optimizer – volume: 15 start-page: 8681 issue: 22 year: 2022 ident: 10.1016/j.apenergy.2024.122955_b42 article-title: Research on size optimization of wave energy converters based on a floating wind-wave combined power generation platform publication-title: Energies doi: 10.3390/en15228681
SSID	ssj0002120
Score	2.5467353
Snippet	Hybrid offshore renewable energy platforms have been proposed to optimise power production and reduce the levelised cost of energy by integrating or...
SourceID	swepub proquest crossref elsevier
SourceType	Open Access Repository Aggregation Database Enrichment Source Index Database Publisher
StartPage	122955
SubjectTerms	acceleration algorithms Australia coasts damping decline energy Genetic algorithms Hybrid wave-wind energy system Hybrid wave-wind energy systems Hybrid waves hybrids income longevity Multi-objective optimisation algorithm Multi-objective optimization algorithm Multi-objectives optimization Multiobjective optimization Offshore oil well production Offshore wind turbine Offshore wind turbines Optimization algorithms power generation Power quality Power takeoffs spring Sustainable energy Swarm intelligence algorithms swarms system optimization water power wave energy Wave energy conversion Wave energy converters Wave wind wind Wind energy systems Wind power wind turbine wind turbines
Title	Enhancing the performance of hybrid wave-wind energy systems through a fast and adaptive chaotic multi-objective swarm optimisation method
URI	https://dx.doi.org/10.1016/j.apenergy.2024.122955 https://www.proquest.com/docview/3153787957 https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-66338
Volume	362
WOSCitedRecordID	wos001219088400001&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-9118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1bb9MwFLZKxwM8IBhMlJuMBE9RShrn-lhBEaBtmrSBxpN14jg0pU2qpmsHP4E_xV_juHbSMA3GHnhJqyjHcXK-2N-xz4WQFzCA2E0QvE4kXNvLPBwH5SCwkdqGaItlTgAbTe-Hh4fR6Wl81On8rGNhVtOwKKLz83j-X1WN51DZKnT2GupuGsUT-B-VjkdUOx7_SfGjYqxyaNRRUK3AAMULv6kILWsNK2mv0Ry3pI790wmdq6ZsD1gZVNr5HFKYb_yLxBhKld5144Nol8lEj5VWtYbFzCrxopnxDTJ1qdvEt2a7-n6t7akxaIcROU7zZrkHr8lxzPla6glAZWnMwfrc38p9H2tsHiASYWYdwCTfyqdgHefT1YYX70tQORms4_5Rv73E4Xpqd14HedahXU5gK1OvPWwzM4rrgXegypL7l84Jenli0oe5fsS-ukV_K_B7Eu4Lk2Pjslh7w0143Q5X7XDdzg2y44Z-HHXJzvD96PRDQwZckxm0foJWkPrlPfoTP2rbP-2cthsedHKX3DEGDB1q4N0jHVnsktuttJa7ZG-0jZ7ES830Ud0nPxpsUsQmbWGTlhnV2KQNNqnuNjXYpAabFKjCJkW10hqb1GCTXsAm3WCTtrFJNTYfkI9vRyev39mmGIgtPOYsbRHEgXDjyJOB6zBImZTRIEhlHIWpRDNCIrMfeAlDm0GgSe5n-OsK4SSR5wNjKdsj3aIs5ENCIZF-NBDABATKXk9YFCYsy4TngapZ2yN-rQIuTKZ8VbBlyv8Ogh551cjNda6YKyXiWsPcMF7NZDmC90rZ5zUkOL5Ctc8HhSzPKs6QxeA8HPthj7zUWGn6o7LJv8k_DXm5-MJn6ZijxcGiR9fu-WNya_ulPiHd5eJMPiU3xWqZV4tn5iv4Be5P8Yg
linkProvider	Elsevier
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=Enhancing+the+performance+of+hybrid+wave-wind+energy+systems+through+a+fast+and+adaptive+chaotic+multi-objective+swarm+optimisation+method&rft.jtitle=Applied+energy&rft.au=Neshat%2C+Mehdi&rft.au=Sergiienko%2C+Nataliia+Y.&rft.au=Nezhad%2C+Meysam+Majidi&rft.au=da+Silva%2C+Leandro+S.P.&rft.date=2024-05-15&rft.issn=0306-2619&rft.volume=362&rft.spage=122955&rft_id=info:doi/10.1016%2Fj.apenergy.2024.122955&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_apenergy_2024_122955
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-2619&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-2619&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-2619&client=summon