MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems
This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biolog...
Uloženo v:
| Vydáno v: | Evolutionary intelligence Ročník 16; číslo 1; s. 169 - 195 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2023
Springer Nature B.V |
| Témata: | |
| ISSN: | 1864-5909, 1864-5917 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biological interaction between predator and prey. The proposed MOMPA can address multiple and conflicting objectives when solving optimization problems. The MOMPA is formulated using elitist non-dominated sorting and crowding distance mechanisms. The proposed method is tested on various multi-objective case studies, including 32 unconstrained, constraint, and engineering design problems with different linear, nonlinear, continuous, and discrete characteristics-based Pareto front problems. The results of the proposed MOMPA are compared with several well-regarded Multi-Objective Water-Cycle Algorithm, Multi-Objective Symbiotic-Organism Search, Multi-Objective Moth-Flame Optimizer algorithms qualitatively and quantitatively using several performance indicators. The experimental results demonstrate the merits of the proposed method. |
|---|---|
| AbstractList | This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and crowding distance mechanism. The proposed algorithm is based on the recently proposed Marine-Predator Algorithm, and it was inspired by biological interaction between predator and prey. The proposed MOMPA can address multiple and conflicting objectives when solving optimization problems. The MOMPA is formulated using elitist non-dominated sorting and crowding distance mechanisms. The proposed method is tested on various multi-objective case studies, including 32 unconstrained, constraint, and engineering design problems with different linear, nonlinear, continuous, and discrete characteristics-based Pareto front problems. The results of the proposed MOMPA are compared with several well-regarded Multi-Objective Water-Cycle Algorithm, Multi-Objective Symbiotic-Organism Search, Multi-Objective Moth-Flame Optimizer algorithms qualitatively and quantitatively using several performance indicators. The experimental results demonstrate the merits of the proposed method. |
| Author | Jangir, Pradeep Manoharan, Premkumar Buch, Hitarth Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Pradeep orcidid: 0000-0001-6944-4775 surname: Jangir fullname: Jangir, Pradeep email: sbp_140310707009@gtu.edu.in, pkjmtech@gmail.com organization: Rajasthan Rajya Vidyut Prasaran Nigam Ltd – sequence: 2 givenname: Hitarth surname: Buch fullname: Buch, Hitarth organization: Government Engineering College – sequence: 3 givenname: Seyedali surname: Mirjalili fullname: Mirjalili, Seyedali organization: Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Yonsei Frontier Lab, Yonsei University – sequence: 4 givenname: Premkumar surname: Manoharan fullname: Manoharan, Premkumar organization: Department of Electrical and Electronics Engineering, Dayananda Sagar College of Engineering |
| BookMark | eNp9kE1LwzAYgINMcJv-AU8Fz9V8tGnqbQy_YGMe9BzS9s3MaJuZZAP3642bKOywUxJ4nrwvzwgNetsDQtcE3xKMiztPKOZ5iilJMeZZme7O0JAInqV5SYrB3x2XF2jk_SpCFBfZEFXzxfx1cp_MN20wqa1WUAezhaRTzvSQrB00KliXqHZpnQkfXaLjy9t2a_pl0h1Zdh1MZ3YqGNtH11YtdP4SnWvVerj6Pcfo_fHhbfqczhZPL9PJLK0ZKUNa6rzmRVM0FQMQoEnBmSAEBC0pr6GqdM01FcAqJoA3PAKAKaZKlbTWWrExujn8Gwd_bsAHubIb18eRktE8zwTLcR4pcaBqZ713oGVtwn7h4JRpJcHyp6g8FJWxqNwXlbuo0iN17UwM9XVaYgfJR7hfgvvf6oT1DYkjjhg |
| CitedBy_id | crossref_primary_10_1016_j_matcom_2024_12_008 crossref_primary_10_3390_app122211829 crossref_primary_10_1016_j_epsr_2024_110925 crossref_primary_10_1016_j_energy_2024_130771 crossref_primary_10_1080_23080477_2025_2501323 crossref_primary_10_3390_w17111612 crossref_primary_10_1109_ACCESS_2025_3529839 crossref_primary_10_1007_s10462_022_10235_z crossref_primary_10_1007_s11831_023_09912_1 crossref_primary_10_1155_er_9531493 crossref_primary_10_3390_biomimetics8080615 crossref_primary_10_1016_j_asej_2025_103590 crossref_primary_10_1007_s40747_024_01418_y crossref_primary_10_1007_s40745_024_00521_8 crossref_primary_10_1177_14727978251364470 crossref_primary_10_1007_s11831_023_09897_x crossref_primary_10_1016_j_cma_2024_116840 crossref_primary_10_3390_w16233475 crossref_primary_10_1371_journal_pone_0301630 crossref_primary_10_1016_j_asoc_2023_110525 crossref_primary_10_1016_j_eswa_2023_122732 crossref_primary_10_1038_s41598_025_96901_y crossref_primary_10_1016_j_energy_2024_133120 crossref_primary_10_1016_j_eswa_2023_122452 crossref_primary_10_1093_jcde_qwad077 crossref_primary_10_1038_s41598_024_52083_7 crossref_primary_10_1109_ACCESS_2022_3215131 crossref_primary_10_1007_s10489_022_03875_9 crossref_primary_10_1038_s41598_025_96263_5 crossref_primary_10_1016_j_egyr_2025_05_056 crossref_primary_10_1016_j_aei_2025_103512 crossref_primary_10_1038_s41598_024_69487_0 crossref_primary_10_1177_14780771251335107 crossref_primary_10_1007_s11831_024_10168_6 crossref_primary_10_1016_j_eswa_2023_119970 crossref_primary_10_1016_j_istruc_2025_109628 crossref_primary_10_1016_j_engappai_2025_111412 crossref_primary_10_1016_j_conbuildmat_2024_135011 crossref_primary_10_1142_S0219649225500157 crossref_primary_10_1016_j_egyr_2024_09_020 crossref_primary_10_1016_j_engappai_2024_107881 crossref_primary_10_1016_j_asoc_2023_111106 crossref_primary_10_1016_j_knosys_2024_111412 crossref_primary_10_1016_j_eswa_2024_126185 crossref_primary_10_1016_j_matcom_2024_01_012 crossref_primary_10_1007_s00500_023_08812_7 crossref_primary_10_1007_s00158_023_03499_8 crossref_primary_10_3390_app13053223 crossref_primary_10_1016_j_procs_2023_11_114 crossref_primary_10_1007_s12065_024_00910_1 crossref_primary_10_1016_j_autcon_2024_105809 crossref_primary_10_1016_j_jksuci_2023_101734 crossref_primary_10_1080_0305215X_2024_2351197 crossref_primary_10_1186_s42162_025_00528_2 crossref_primary_10_1007_s11042_023_16633_x crossref_primary_10_1080_10589759_2024_2349243 crossref_primary_10_1016_j_eswa_2023_123044 crossref_primary_10_1016_j_eswa_2025_129542 crossref_primary_10_1109_ACCESS_2022_3209996 |
| Cites_doi | 10.1007/s00500-013-1187-3 10.1109/ICACCI.2016.7732428 10.1109/ACCESS.2020.3047936 10.1016/j.advengsoft.2017.07.002 10.5267/j.dsl.2019.8.001 10.1080/0305215X.2011.604316 10.1162/evco.1994.2.3.221 10.1016/j.asoc.2016.04.030 10.1109/ACCESS.2021.3085529 10.1016/j.ins.2015.10.010 10.1109/4235.996017 10.1109/TEVC.2004.826067 10.1023/A:1015516501242 10.1007/s10489-016-0825-8 10.1016/j.engappai.2017.04.018 10.1007/s12293-011-0072-9 10.1007/s00158-005-0527-z 10.1007/s12293-017-0237-2 10.1016/j.engappai.2018.04.018 10.1016/j.swevo.2011.08.001 10.1109/CEC.2002.1004388 10.1109/TEVC.2008.925798 10.1162/106365600568202 10.1007/s10489-017-1019-8 10.1016/j.eswa.2020.113377 10.1080/03052150108940926 10.1109/MCI.2017.2742868 10.1016/j.asoc.2014.10.042 10.1080/03052150210915 10.1007/b106458 10.1016/j.engappai.2012.11.006 10.1007/s11390-008-9114-2 10.1016/j.cma.2021.114029 10.1109/ACCESS.2021.3087739 10.1109/4235.585893 10.1016/j.knosys.2017.07.018 10.1080/03052150903505877 10.1109/TEVC.2015.2504730 10.1016/j.knosys.2021.106856 10.1007/978-3-540-31880-4_39 10.1109/TCYB.2013.2282503 10.1109/ACCESS.2021.3066323 10.1007/s00521-015-1920-1 10.1007/978-3-540-72964-8_12 10.1016/j.eswa.2015.10.039 10.1007/BF01743536 10.3233/JAE-2007-913 |
| ContentType | Journal Article |
| Copyright | The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. |
| Copyright_xml | – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 – notice: The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021. |
| DBID | AAYXX CITATION 7XB 8FE 8FG ABJCF AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO GNUQQ HCIFZ JQ2 K7- L6V M2P M7S P5Z P62 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS Q9U |
| DOI | 10.1007/s12065-021-00649-z |
| DatabaseName | CrossRef ProQuest Central (purchase pre-March 2016) ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central UK/Ireland ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central ProQuest Central Student ProQuest SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Engineering Collection ProQuest Science Database (NC LIVE) ProQuest Engineering Database (NC LIVE) ProQuest Advanced Technologies & Aerospace Database (NC LIVE) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic (New) ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection ProQuest Central Basic |
| DatabaseTitle | CrossRef Computer Science Database ProQuest Central Student Technology Collection ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest Central Korea ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Engineering Database ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Materials Science & Engineering Collection ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | Computer Science Database |
| Database_xml | – sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science |
| EISSN | 1864-5917 |
| EndPage | 195 |
| ExternalDocumentID | 10_1007_s12065_021_00649_z |
| GroupedDBID | -5B -5G -BR -EM -Y2 -~C .86 06D 0R~ 0VY 1N0 203 29G 29~ 2JN 2JY 2KG 2VQ 2~H 30V 4.4 406 408 409 40D 5GY 5VS 67Z 6NX 875 8TC 8UJ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBXA ABDZT ABECU ABFTD ABFTV ABHQN ABJNI ABJOX ABKCH ABMNI ABMQK ABQBU ABSXP ABTEG ABTHY ABTKH ABTMW ABULA ABWNU ABXPI ACAOD ACDTI ACGFS ACHSB ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACZOJ ADHHG ADHIR ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFGCZ AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGJBK AGMZJ AGQEE AGQMX AGRTI AGWZB AGYKE AHAVH AHBYD AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALFXC ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR ANMIH AOCGG AUKKA AXYYD AYJHY B-. BA0 BDATZ BGNMA CAG COF CS3 CSCUP DDRTE DNIVK DPUIP EBLON EBS EIOEI EJD ESBYG F5P FERAY FFXSO FIGPU FINBP FNLPD FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNWQR GQ6 GQ7 GQ8 GXS H13 HF~ HG5 HG6 HLICF HMJXF HQYDN HRMNR HZ~ I0C IJ- IKXTQ IWAJR IXC IXD IZIGR IZQ I~X J-C J0Z JBSCW JCJTX JZLTJ KOV LLZTM M4Y MA- NPVJJ NQJWS NU0 O9- O93 O9J OAM P2P P9P PT4 QOS R89 RLLFE ROL RPX RSV S16 S1Z S27 S3B SAP SDH SEG SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE T13 TSG TSK U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W48 WK8 YLTOR Z45 ZMTXR ~A9 AAPKM AAYXX ABBRH ABDBE ABFSG ABJCF ABRTQ ACSTC ADKFA AEZWR AFDZB AFFHD AFHIU AFKRA AFOHR AHPBZ AHWEU AIXLP ARAPS ATHPR AYFIA AZQEC BENPR BGLVJ CCPQU CITATION DWQXO GNUQQ HCIFZ K7- M2P M7S PHGZM PHGZT PQGLB PTHSS 7XB 8FE 8FG JQ2 L6V P62 PKEHL PQEST PQQKQ PQUKI PRINS Q9U |
| ID | FETCH-LOGICAL-c319t-9f5c67d7db3ee8ef1763811e82926cebbfc6f28e3b38e6d6f17e0202aa92cffa3 |
| IEDL.DBID | M2P |
| ISICitedReferencesCount | 87 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000685361200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1864-5909 |
| IngestDate | Tue Sep 30 03:41:01 EDT 2025 Sat Nov 29 06:12:14 EST 2025 Tue Nov 18 22:43:18 EST 2025 Fri Feb 21 02:44:24 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Crowding distance Multi-objective optimization Real-world engineering problems Metaheuristics Non-dominated sorting mechanism |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c319t-9f5c67d7db3ee8ef1763811e82926cebbfc6f28e3b38e6d6f17e0202aa92cffa3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0001-6944-4775 |
| PQID | 3255483505 |
| PQPubID | 2043920 |
| PageCount | 27 |
| ParticipantIDs | proquest_journals_3255483505 crossref_citationtrail_10_1007_s12065_021_00649_z crossref_primary_10_1007_s12065_021_00649_z springer_journals_10_1007_s12065_021_00649_z |
| PublicationCentury | 2000 |
| PublicationDate | 20230200 2023-02-00 20230201 |
| PublicationDateYYYYMMDD | 2023-02-01 |
| PublicationDate_xml | – month: 2 year: 2023 text: 20230200 |
| PublicationDecade | 2020 |
| PublicationPlace | Berlin/Heidelberg |
| PublicationPlace_xml | – name: Berlin/Heidelberg – name: Heidelberg |
| PublicationTitle | Evolutionary intelligence |
| PublicationTitleAbbrev | Evol. Intel |
| PublicationYear | 2023 |
| Publisher | Springer Berlin Heidelberg Springer Nature B.V |
| Publisher_xml | – name: Springer Berlin Heidelberg – name: Springer Nature B.V |
| References | MirjaliliSJangirPSaremiSMulti-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problemsAppl Intell201746799510.1007/s10489-016-0825-8 MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Comput Appl2016271053107310.1007/s00521-015-1920-1 Coello Coello, C. A., & Lechuga, M. S. MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600). 2:1051–1056. ZengGQChenJLiLMChenMRWuLDaiYXZhengCWAn improved multi-objective population-based extremal optimization algorithm with polynomial mutationInf Sci2016330497310.1016/j.ins.2015.10.010 DebKDattaRHybrid evolutionary multi-objective optimization and analysis of machining operationsEng Optim20124468570610.1080/0305215X.2011.604316 MirjaliliSeyedaliSaremiShahrzadMirjaliliSeyed MohammadCoelhoLeandro dos S.Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimizationExpert Systems with Applications20164710611910.1016/j.eswa.2015.10.039 Mohamed, A. B., Reda, M., Seyedali, M., Ripon, K. C., & Michael, R. (2021). An Efficient Marine Predators Algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations. IEEE Access 9:42817-42844. Vikas & Nanda, S. J. (2016) Multi-objective Moth Flame Optimization. In Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2470-2476. Long, C., Xuebing, C., Kezhong, J., & Zhenzhou, T. (2021). MOMPA: A high performance multi-objective optimizer based on marine predator algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '21). Association for Computing Machinery, New York, NY, USA, 177–178. MoussouniFBrissetSBrochetPSome results on the design of brushless DC wheel motor using SQP and GAInt J Appl Electromagn Mech20072623324110.3233/JAE-2007-913 FaramarziAHeidarinejadMMirjaliliSGandomiAHMarine Predators Algorithm: a nature-inspired metaheuristicExpert Syst Appl202015211337711337710.1016/j.eswa.2020.113377 NguyenLBuiLTAbbassHADMEA-II: the direction-based multi-objective evolutionary algorithm-IISoft Comput2014182119213410.1007/s00500-013-1187-3 Sarker R, Coello Coello C. A. (2003). Assessment Methodologies for Multiobjective Evolutionary Algorithms. In: Evolutionary Optimization. International Series in Operations Research & Management Science, vol 48. Springer, Boston, MA. Binh, T. T., & Korn, U. (1997). MOBES: A multiobjective evolution strategy for constrained optimization problems. Proc. 3rd - Int. Mendel Con. Genetic Algorithms, 176–182. Jeong, M. J., Kobayashi, T., & Yoshimura, S. (2005). Extraction of design characteristics of multiobjective optimization–its application to design of artificial satellite heat pipe. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 561–575): Springer. BuiLTDebKAbbassHAEssamDInterleaving guidance in evolutionary multi-objective optimizationJ Comput Sci Technol200823446310.1007/s11390-008-9114-2 Coello CoelloCAPulidoGTMultiobjective structural optimization using a microgenetic algorithmStruct Multidiscip Optim20053038840310.1007/s00158-005-0527-z DebKSrinivasanAInnovization: discovery of innovative design principles through multiobjective evolutionary optimizationMultiobjective Probl Solving Nat200810.1007/978-3-540-72964-8_12 BuchHTrivediINA new non-dominated sorting ions motion algorithm: development and applicationsDecis Sci Lett20209597610.5267/j.dsl.2019.8.001 KotinisMA particle swarm optimizer for constrained multi-objective engineering design problemsEng Optim20104290792610.1080/03052150903505877 LiHZhangQMultiobjective optimization problems with complicated Pareto sets, MOEA/ D and NSGA-IIIEEE Trans Evol Comput20091328430210.1109/TEVC.2008.925798 SrinivasanNDebKMulti-objective function optimisation using non-dominated sorting genetic algorithmEvolutionary Comp1994222124810.1162/evco.1994.2.3.221 Zhang, Q., Zhou, A., Zhao, S., Suganthan, P. N., Liu, W., & Tiwari, S. (2008). Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, 264. MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp Swarm Algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw201711416319110.1016/j.advengsoft.2017.07.002 Deb K., Thiele L., Laumanns M., Zitzler E. (2005) Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham A., Jain L., Goldberg R. (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London. Sumit, K., Pradeep, J., Tejani, G. G., Premkumar, M., Hassan Haes, A. (2021) MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems. IEEE Access 9:84982–85016. DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans Evol Comput2002618219710.1109/4235.996017 YuCLLuYZChuJMulti-objective optimization with combination of particle swarm and extremal optimization for constrained engineering designWSEAS Trans Syst Control20127129138 RayTTaiKSeowKCMultiobjective design optimization by an evolutionary algorithmEng Optim20013339942410.1080/03052150108940926 ZhangMWangHCuiZChenJHybrid multi-objective cuckoo search with dynamical local searchMemet Comput20181019920810.1007/s12293-017-0237-2 MirjaliliSJangirPMirjaliliSZSaremiSTrivediINOptimization of problems with multiple objectives using the multi-verse optimization algorithmKnowl-Based Syst2017134507110.1016/j.knosys.2017.07.018 PremkumarManoharanJangirPradeepSowmyaRavichandranMOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problemsKnowledge-Based Systems202121810685610.1016/j.knosys.2021.106856 JangirPJangirNA new Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) algorithm: development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind powerEng Appl Artif Intell20187244946710.1016/j.engappai.2018.04.018 ZitzlerEDebKThieleLComparison of multiobjective evolutionary algorithms: empirical resultsEvol Comput2000817319510.1162/106365600568202 Tan, K., Lee, T. & Khor, E. (2002) Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons. Artificial Intelligence Review 17:251–290. Li, M., Yang, S., & Liu, X. (2015). Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization. IEEE Trans Evol Comput 20:645–665. SadollahAEskandarHKimJHWater cycle algorithm for solving constrained multi-objective optimization problemsAppl Soft Comput J20152727929810.1016/j.asoc.2014.10.042 WolpertDHHMacreadyWGGNo free lunch theorems for optimizationIEEE Trans Evol Comput19971678210.1109/4235.585893 PandaAPaniSA symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problemsAppl Soft Comput J20164634436010.1016/j.asoc.2016.04.030 AkbariRHedayatzadehRZiaratiKHassanizadehBA multi-objective artificial bee colony algorithmSwarm Evolut Comput20122395210.1016/j.swevo.2011.08.001 NafchiAMMoradiAConstrained multi-objective optimization problems in mechanical engineering design using bees algorithmJ Solid Mech20113353364 Sierra, M.R., Coello Coello, C. A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. In: Coello Coello C.A., Hernández Aguirre A., Zitzler E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization. Master of Science Thesis, Massachusetts Institute of Technology, Cambridge, MA 02139, United States. BuiLTLiuJBenderABarlowMWesolkowskiSAbbassHADmea: a direction-based multiobjective evolutionary algorithmMemetic Computing2011327128510.1007/s12293-011-0072-9 Deb, K., & Srinivasan, A. (2006). Monotonicity Analysis, Evolutionary Multi-Objective Optimization, and Discovery of Design Principles. In KanGAL Report Number 2006004:1-12. TianYChengRZhangXJinYPlatEMO: a MATLAB platform for evolutionary multi-objective optimization [educational forum]IEEE Comput Intell Mag201712738710.1109/MCI.2017.2742868 SavsaniVTawhidMANon-dominated sorting moth flame optimization (NS-MFO) for multi-objective problemsEng Appl Artif Intell201763203210.1016/j.engappai.2017.04.018 Premkumar, M., Pradeep, J., Santhosh Kumar, B., Sowmya, R., Alhelou, H. H., Abualigah, L., Yildiz., A. R., Mirjalili, S. (2021) A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations. IEEE Access 9:84263-84295. RayTLiewKMA swarm metaphor for multiobjective design optimizationEng Optim20023414115310.1080/03052150210915 ZhongKeyuZhouGuoDengWuZhouYongquanLuoQifangMOMPA: Multi-objective marine predator algorithmComputer Methods in Applied Mechanics and Engineering202138511402910.1016/j.cma.2021.11402907415675 Coello CoelloCAPulidoGTLechugaMSHandling multiple objectives with particle swarm optimizationIEEE Trans Evol Comput2004825627910.1109/TEVC.2004.826067 MirjaliliSZSMirjaliliSZSSaremiSFarisHAljarahIGrasshopper optimization algorithm for multi-objective optimization problemsAppl Intell20184880582010.1007/s10489-017-1019-8 ZouFWangLHeiXChenDWangBMulti-objective optimization using teaching-learning-based optimization algorithmEng Appl Artif Intell2013261291130010.1016/j.engappai.2012.11.006 OsyczkaAKunduSA new method to solve generalized multicriteria optimization problems using the simple genetic algorithmStruct Optim199510 Manoharan Premkumar (649_CR3) 2021; 218 S Mirjalili (649_CR10) 2017; 114 649_CR2 649_CR1 T Ray (649_CR50) 2001; 33 A Faramarzi (649_CR23) 2020; 152 649_CR21 H Li (649_CR30) 2009; 13 K Deb (649_CR41) 2012; 44 649_CR25 649_CR26 T Ray (649_CR49) 2002; 34 S Mirjalili (649_CR15) 2016; 27 649_CR29 L Nguyen (649_CR32) 2014; 18 GQ Zeng (649_CR34) 2016; 330 M Kotinis (649_CR46) 2010; 42 Y Tian (649_CR55) 2017; 12 V Savsani (649_CR20) 2017; 63 E Zitzler (649_CR36) 2000; 8 F Zou (649_CR17) 2013; 26 CL Yu (649_CR51) 2012; 7 LT Bui (649_CR28) 2011; 3 F Moussouni (649_CR47) 2007; 26 AM Nafchi (649_CR48) 2011; 3 649_CR54 CA Coello Coello (649_CR42) 2005; 30 S Mirjalili (649_CR12) 2017; 46 649_CR52 649_CR53 649_CR56 649_CR6 649_CR4 N Srinivasan (649_CR9) 1994; 2 M Zhang (649_CR16) 2018; 10 H Buch (649_CR22) 2020; 9 LT Bui (649_CR27) 2008; 23 P Jangir (649_CR13) 2018; 72 K Deb (649_CR44) 2008 A Osyczka (649_CR39) 1995; 10 649_CR43 R Akbari (649_CR18) 2012; 2 CA Coello Coello (649_CR7) 2004; 8 649_CR45 DHH Wolpert (649_CR5) 1997; 1 Keyu Zhong (649_CR24) 2021; 385 S Mirjalili (649_CR11) 2017; 134 A Panda (649_CR33) 2016; 46 K Deb (649_CR8) 2002; 6 A Sadollah (649_CR40) 2015; 27 Seyedali Mirjalili (649_CR14) 2016; 47 649_CR31 M Li (649_CR38) 2014; 44 649_CR37 649_CR35 SZS Mirjalili (649_CR19) 2018; 48 |
| References_xml | – reference: Premkumar, M., Pradeep, J., Santhosh Kumar, B., Sowmya, R., Alhelou, H. H., Abualigah, L., Yildiz., A. R., Mirjalili, S. (2021) A New Arithmetic Optimization Algorithm for Solving Real-World Multiobjective CEC-2021 Constrained Optimization Problems: Diversity Analysis and Validations. IEEE Access 9:84263-84295. – reference: LiHZhangQMultiobjective optimization problems with complicated Pareto sets, MOEA/ D and NSGA-IIIEEE Trans Evol Comput20091328430210.1109/TEVC.2008.925798 – reference: NguyenLBuiLTAbbassHADMEA-II: the direction-based multi-objective evolutionary algorithm-IISoft Comput2014182119213410.1007/s00500-013-1187-3 – reference: SadollahAEskandarHKimJHWater cycle algorithm for solving constrained multi-objective optimization problemsAppl Soft Comput J20152727929810.1016/j.asoc.2014.10.042 – reference: LiMYangSLiKLiuXEvolutionary algorithms with segment-based search for multiobjective optimization problemsIEEE Trans Cybern2014441295131310.1109/TCYB.2013.2282503 – reference: OsyczkaAKunduSA new method to solve generalized multicriteria optimization problems using the simple genetic algorithmStruct Optim199510949910.1007/BF01743536 – reference: ZitzlerEDebKThieleLComparison of multiobjective evolutionary algorithms: empirical resultsEvol Comput2000817319510.1162/106365600568202 – reference: Sumit, K., Pradeep, J., Tejani, G. G., Premkumar, M., Hassan Haes, A. (2021) MOPGO: A New Physics-Based Multi-Objective Plasma Generation Optimizer for Solving Structural Optimization Problems. IEEE Access 9:84982–85016. – reference: Coello CoelloCAPulidoGTLechugaMSHandling multiple objectives with particle swarm optimizationIEEE Trans Evol Comput2004825627910.1109/TEVC.2004.826067 – reference: PandaAPaniSA symbiotic organisms search algorithm with adaptive penalty function to solve multi-objective constrained optimization problemsAppl Soft Comput J20164634436010.1016/j.asoc.2016.04.030 – reference: DebKDattaRHybrid evolutionary multi-objective optimization and analysis of machining operationsEng Optim20124468570610.1080/0305215X.2011.604316 – reference: NafchiAMMoradiAConstrained multi-objective optimization problems in mechanical engineering design using bees algorithmJ Solid Mech20113353364 – reference: RayTTaiKSeowKCMultiobjective design optimization by an evolutionary algorithmEng Optim20013339942410.1080/03052150108940926 – reference: JangirPJangirNA new Non-Dominated Sorting Grey Wolf Optimizer (NS-GWO) algorithm: development and application to solve engineering designs and economic constrained emission dispatch problem with integration of wind powerEng Appl Artif Intell20187244946710.1016/j.engappai.2018.04.018 – reference: Coello Coello, C. A., & Lechuga, M. S. MOPSO: a proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600). 2:1051–1056. – reference: PremkumarManoharanJangirPradeepSowmyaRavichandranMOGBO: A new Multiobjective Gradient-Based Optimizer for real-world structural optimization problemsKnowledge-Based Systems202121810685610.1016/j.knosys.2021.106856 – reference: MirjaliliSeyedaliSaremiShahrzadMirjaliliSeyed MohammadCoelhoLeandro dos S.Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimizationExpert Systems with Applications20164710611910.1016/j.eswa.2015.10.039 – reference: Sierra, M.R., Coello Coello, C. A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. In: Coello Coello C.A., Hernández Aguirre A., Zitzler E. (eds) Evolutionary Multi-Criterion Optimization. EMO 2005. Lecture Notes in Computer Science, vol 3410. Springer, Berlin, Heidelberg. – reference: MoussouniFBrissetSBrochetPSome results on the design of brushless DC wheel motor using SQP and GAInt J Appl Electromagn Mech20072623324110.3233/JAE-2007-913 – reference: MirjaliliSZSMirjaliliSZSSaremiSFarisHAljarahIGrasshopper optimization algorithm for multi-objective optimization problemsAppl Intell20184880582010.1007/s10489-017-1019-8 – reference: Sarker R, Coello Coello C. A. (2003). Assessment Methodologies for Multiobjective Evolutionary Algorithms. In: Evolutionary Optimization. International Series in Operations Research & Management Science, vol 48. Springer, Boston, MA. – reference: ZengGQChenJLiLMChenMRWuLDaiYXZhengCWAn improved multi-objective population-based extremal optimization algorithm with polynomial mutationInf Sci2016330497310.1016/j.ins.2015.10.010 – reference: MirjaliliSJangirPMirjaliliSZSaremiSTrivediINOptimization of problems with multiple objectives using the multi-verse optimization algorithmKnowl-Based Syst2017134507110.1016/j.knosys.2017.07.018 – reference: SavsaniVTawhidMANon-dominated sorting moth flame optimization (NS-MFO) for multi-objective problemsEng Appl Artif Intell201763203210.1016/j.engappai.2017.04.018 – reference: BuchHTrivediINA new non-dominated sorting ions motion algorithm: development and applicationsDecis Sci Lett20209597610.5267/j.dsl.2019.8.001 – reference: Li, M., Yang, S., & Liu, X. (2015). Pareto or non-Pareto: Bi-criterion evolution in multi-objective optimization. IEEE Trans Evol Comput 20:645–665. – reference: WolpertDHHMacreadyWGGNo free lunch theorems for optimizationIEEE Trans Evol Comput19971678210.1109/4235.585893 – reference: YuCLLuYZChuJMulti-objective optimization with combination of particle swarm and extremal optimization for constrained engineering designWSEAS Trans Syst Control20127129138 – reference: AkbariRHedayatzadehRZiaratiKHassanizadehBA multi-objective artificial bee colony algorithmSwarm Evolut Comput20122395210.1016/j.swevo.2011.08.001 – reference: Binh, T. T., & Korn, U. (1997). MOBES: A multiobjective evolution strategy for constrained optimization problems. Proc. 3rd - Int. Mendel Con. Genetic Algorithms, 176–182. – reference: RayTLiewKMA swarm metaphor for multiobjective design optimizationEng Optim20023414115310.1080/03052150210915 – reference: Tan, K., Lee, T. & Khor, E. (2002) Evolutionary Algorithms for Multi-Objective Optimization: Performance Assessments and Comparisons. Artificial Intelligence Review 17:251–290. – reference: TianYChengRZhangXJinYPlatEMO: a MATLAB platform for evolutionary multi-objective optimization [educational forum]IEEE Comput Intell Mag201712738710.1109/MCI.2017.2742868 – reference: DebKPratapAAgarwalSMeyarivanTA fast and elitist multiobjective genetic algorithm: NSGA-IIIEEE Trans Evol Comput2002618219710.1109/4235.996017 – reference: ZhongKeyuZhouGuoDengWuZhouYongquanLuoQifangMOMPA: Multi-objective marine predator algorithmComputer Methods in Applied Mechanics and Engineering202138511402910.1016/j.cma.2021.11402907415675 – reference: ZhangMWangHCuiZChenJHybrid multi-objective cuckoo search with dynamical local searchMemet Comput20181019920810.1007/s12293-017-0237-2 – reference: Vikas & Nanda, S. J. (2016) Multi-objective Moth Flame Optimization. In Proceedings of International Conference on Advances in Computing, Communications and Informatics (ICACCI). 2470-2476. – reference: Zhang, Q., Zhou, A., Zhao, S., Suganthan, P. N., Liu, W., & Tiwari, S. (2008). Multiobjective optimization test instances for the CEC 2009 special session and competition. University of Essex, Colchester, UK and Nanyang technological University, Singapore, special session on performance assessment of multi-objective optimization algorithms, technical report, 264. – reference: Mohamed, A. B., Reda, M., Seyedali, M., Ripon, K. C., & Michael, R. (2021). An Efficient Marine Predators Algorithm for Solving Multi-Objective Optimization Problems: Analysis and Validations. IEEE Access 9:42817-42844. – reference: Deb K., Thiele L., Laumanns M., Zitzler E. (2005) Scalable Test Problems for Evolutionary Multiobjective Optimization. In: Abraham A., Jain L., Goldberg R. (eds) Evolutionary Multiobjective Optimization. Advanced Information and Knowledge Processing. Springer, London. – reference: DebKSrinivasanAInnovization: discovery of innovative design principles through multiobjective evolutionary optimizationMultiobjective Probl Solving Nat200810.1007/978-3-540-72964-8_12 – reference: Jeong, M. J., Kobayashi, T., & Yoshimura, S. (2005). Extraction of design characteristics of multiobjective optimization–its application to design of artificial satellite heat pipe. In International Conference on Evolutionary Multi-Criterion Optimization (pp. 561–575): Springer. – reference: BuiLTLiuJBenderABarlowMWesolkowskiSAbbassHADmea: a direction-based multiobjective evolutionary algorithmMemetic Computing2011327128510.1007/s12293-011-0072-9 – reference: Deb, K., & Srinivasan, A. (2006). Monotonicity Analysis, Evolutionary Multi-Objective Optimization, and Discovery of Design Principles. In KanGAL Report Number 2006004:1-12. – reference: SrinivasanNDebKMulti-objective function optimisation using non-dominated sorting genetic algorithmEvolutionary Comp1994222124810.1162/evco.1994.2.3.221 – reference: BuiLTDebKAbbassHAEssamDInterleaving guidance in evolutionary multi-objective optimizationJ Comput Sci Technol200823446310.1007/s11390-008-9114-2 – reference: MirjaliliSJangirPSaremiSMulti-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problemsAppl Intell201746799510.1007/s10489-016-0825-8 – reference: ZouFWangLHeiXChenDWangBMulti-objective optimization using teaching-learning-based optimization algorithmEng Appl Artif Intell2013261291130010.1016/j.engappai.2012.11.006 – reference: MirjaliliSGandomiAHMirjaliliSZSaremiSFarisHMirjaliliSMSalp Swarm Algorithm: a bio-inspired optimizer for engineering design problemsAdv Eng Softw201711416319110.1016/j.advengsoft.2017.07.002 – reference: KotinisMA particle swarm optimizer for constrained multi-objective engineering design problemsEng Optim20104290792610.1080/03052150903505877 – reference: Long, C., Xuebing, C., Kezhong, J., & Zhenzhou, T. (2021). MOMPA: A high performance multi-objective optimizer based on marine predator algorithm. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '21). Association for Computing Machinery, New York, NY, USA, 177–178. – reference: MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Comput Appl2016271053107310.1007/s00521-015-1920-1 – reference: Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization. Master of Science Thesis, Massachusetts Institute of Technology, Cambridge, MA 02139, United States. – reference: Premkumar, M., Pradeep, J., Sowmya, R., Hassan Haes, A., Ali Asghar, H., & Huiling, C. (2021) MOSMA: Multi-Objective Slime Mould Algorithm Based on Elitist Non-Dominated Sorting. IEEE Access 9:3229–3248. – reference: FaramarziAHeidarinejadMMirjaliliSGandomiAHMarine Predators Algorithm: a nature-inspired metaheuristicExpert Syst Appl202015211337711337710.1016/j.eswa.2020.113377 – reference: Coello CoelloCAPulidoGTMultiobjective structural optimization using a microgenetic algorithmStruct Multidiscip Optim20053038840310.1007/s00158-005-0527-z – volume: 18 start-page: 2119 year: 2014 ident: 649_CR32 publication-title: Soft Comput doi: 10.1007/s00500-013-1187-3 – ident: 649_CR21 doi: 10.1109/ICACCI.2016.7732428 – ident: 649_CR4 doi: 10.1109/ACCESS.2020.3047936 – volume: 114 start-page: 163 year: 2017 ident: 649_CR10 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2017.07.002 – volume: 9 start-page: 59 year: 2020 ident: 649_CR22 publication-title: Decis Sci Lett doi: 10.5267/j.dsl.2019.8.001 – volume: 44 start-page: 685 year: 2012 ident: 649_CR41 publication-title: Eng Optim doi: 10.1080/0305215X.2011.604316 – volume: 2 start-page: 221 year: 1994 ident: 649_CR9 publication-title: Evolutionary Comp doi: 10.1162/evco.1994.2.3.221 – volume: 46 start-page: 344 year: 2016 ident: 649_CR33 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2016.04.030 – ident: 649_CR1 doi: 10.1109/ACCESS.2021.3085529 – volume: 330 start-page: 49 year: 2016 ident: 649_CR34 publication-title: Inf Sci doi: 10.1016/j.ins.2015.10.010 – volume: 6 start-page: 182 year: 2002 ident: 649_CR8 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.996017 – volume: 8 start-page: 256 year: 2004 ident: 649_CR7 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2004.826067 – ident: 649_CR52 doi: 10.1023/A:1015516501242 – volume: 46 start-page: 79 year: 2017 ident: 649_CR12 publication-title: Appl Intell doi: 10.1007/s10489-016-0825-8 – volume: 63 start-page: 20 year: 2017 ident: 649_CR20 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2017.04.018 – volume: 3 start-page: 271 year: 2011 ident: 649_CR28 publication-title: Memetic Computing doi: 10.1007/s12293-011-0072-9 – volume: 30 start-page: 388 year: 2005 ident: 649_CR42 publication-title: Struct Multidiscip Optim doi: 10.1007/s00158-005-0527-z – ident: 649_CR56 – volume: 10 start-page: 199 year: 2018 ident: 649_CR16 publication-title: Memet Comput doi: 10.1007/s12293-017-0237-2 – ident: 649_CR35 – volume: 3 start-page: 353 year: 2011 ident: 649_CR48 publication-title: J Solid Mech – ident: 649_CR25 – volume: 72 start-page: 449 year: 2018 ident: 649_CR13 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2018.04.018 – ident: 649_CR29 – volume: 2 start-page: 39 year: 2012 ident: 649_CR18 publication-title: Swarm Evolut Comput doi: 10.1016/j.swevo.2011.08.001 – ident: 649_CR6 doi: 10.1109/CEC.2002.1004388 – volume: 13 start-page: 284 year: 2009 ident: 649_CR30 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2008.925798 – volume: 8 start-page: 173 year: 2000 ident: 649_CR36 publication-title: Evol Comput doi: 10.1162/106365600568202 – volume: 48 start-page: 805 year: 2018 ident: 649_CR19 publication-title: Appl Intell doi: 10.1007/s10489-017-1019-8 – volume: 7 start-page: 129 year: 2012 ident: 649_CR51 publication-title: WSEAS Trans Syst Control – volume: 152 start-page: 113377 year: 2020 ident: 649_CR23 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2020.113377 – ident: 649_CR53 – volume: 33 start-page: 399 year: 2001 ident: 649_CR50 publication-title: Eng Optim doi: 10.1080/03052150108940926 – volume: 12 start-page: 73 year: 2017 ident: 649_CR55 publication-title: IEEE Comput Intell Mag doi: 10.1109/MCI.2017.2742868 – volume: 27 start-page: 279 year: 2015 ident: 649_CR40 publication-title: Appl Soft Comput J doi: 10.1016/j.asoc.2014.10.042 – volume: 34 start-page: 141 year: 2002 ident: 649_CR49 publication-title: Eng Optim doi: 10.1080/03052150210915 – ident: 649_CR54 doi: 10.1007/b106458 – volume: 26 start-page: 1291 year: 2013 ident: 649_CR17 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2012.11.006 – volume: 23 start-page: 44 year: 2008 ident: 649_CR27 publication-title: J Comput Sci Technol doi: 10.1007/s11390-008-9114-2 – ident: 649_CR43 – volume: 385 start-page: 114029 year: 2021 ident: 649_CR24 publication-title: Computer Methods in Applied Mechanics and Engineering doi: 10.1016/j.cma.2021.114029 – ident: 649_CR2 doi: 10.1109/ACCESS.2021.3087739 – volume: 1 start-page: 67 year: 1997 ident: 649_CR5 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.585893 – ident: 649_CR37 – volume: 134 start-page: 50 year: 2017 ident: 649_CR11 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2017.07.018 – volume: 42 start-page: 907 year: 2010 ident: 649_CR46 publication-title: Eng Optim doi: 10.1080/03052150903505877 – ident: 649_CR31 doi: 10.1109/TEVC.2015.2504730 – volume: 218 start-page: 106856 year: 2021 ident: 649_CR3 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2021.106856 – ident: 649_CR45 doi: 10.1007/978-3-540-31880-4_39 – volume: 44 start-page: 1295 year: 2014 ident: 649_CR38 publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2013.2282503 – ident: 649_CR26 doi: 10.1109/ACCESS.2021.3066323 – volume: 27 start-page: 1053 year: 2016 ident: 649_CR15 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1920-1 – year: 2008 ident: 649_CR44 publication-title: Multiobjective Probl Solving Nat doi: 10.1007/978-3-540-72964-8_12 – volume: 47 start-page: 106 year: 2016 ident: 649_CR14 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2015.10.039 – volume: 10 start-page: 94 year: 1995 ident: 649_CR39 publication-title: Struct Optim doi: 10.1007/BF01743536 – volume: 26 start-page: 233 year: 2007 ident: 649_CR47 publication-title: Int J Appl Electromagn Mech doi: 10.3233/JAE-2007-913 |
| SSID | ssj0062074 |
| Score | 2.536586 |
| Snippet | This paper proposes a new multi-objective algorithm, called Multi-Objective Marine-Predator Algorithm (MOMPA), dependent on elitist non-dominated sorting and... |
| SourceID | proquest crossref springer |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 169 |
| SubjectTerms | Algorithms Applications of Mathematics Archives & records Artificial Intelligence Benchmarks Bioinformatics Control Crowding Design engineering Engineering Heuristic Linear programming Mathematical and Computational Engineering Mechatronics Multiple objective analysis Objectives Optimization Pareto optimization Pareto optimum Predators Research Paper Robotics Statistical Physics and Dynamical Systems Success |
| SummonAdditionalLinks | – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT8MwDLZ4HeDAGzEYKAduEGlNmzbhhhCICzDxErcqSR0Y2kvb4MCvJ-lSBgiQ4NwkiuzE_tzY_gD2lDWaF1bSIsaEJpwhdTiOUyuwMHFkUi1MSTaRXVyI-3vZDEVhwyrbvXqSLC31pNiNOXdJfUqB96OSvk7DrHN3whM2XF3fVfY3ZY2y93Ik0oRy2ZChVOb7NT67ownG_PIsWnqb06X_7XMZFgO6JEfj47ACU9hdhaWKuYGEi7wKCx_aEK6BPr88bx4dkrIYl_b009gIko7ypYGkP8DCh-ZEtR96g9bosUMc0iXu0PqfEaTzZVbPGaFOqO4kga9muA63pyc3x2c0cC9QpyE5otJyk2ZFVugYUaCNnB0SUYSCSZYa1Nqa1DKBsY4FpkXqBqBDnkwpyYy1Kt6AmW6vi5tAMqMj63Cn85GJCwa55CrhqBqoWKHiLKtBVKkgN6ExuefHaOeTlspepLkTaV6KNH-twf77nP64Lcevo-uVZvNwRYd57IKpxOHPBq_BQaXJyeefV9v62_BtmPcU9eNM7zrMjAbPuANz5mXUGg52y6P7BnL36x0 priority: 102 providerName: Springer Nature |
| Title | MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems |
| URI | https://link.springer.com/article/10.1007/s12065-021-00649-z https://www.proquest.com/docview/3255483505 |
| Volume | 16 |
| WOSCitedRecordID | wos000685361200001&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: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 1864-5917 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0062074 issn: 1864-5909 databaseCode: P5Z dateStart: 20230201 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1864-5917 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0062074 issn: 1864-5909 databaseCode: K7- dateStart: 20230201 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 1864-5917 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0062074 issn: 1864-5909 databaseCode: M7S dateStart: 20230201 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1864-5917 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0062074 issn: 1864-5909 databaseCode: BENPR dateStart: 20230201 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Science Database customDbUrl: eissn: 1864-5917 dateEnd: 20241209 omitProxy: false ssIdentifier: ssj0062074 issn: 1864-5909 databaseCode: M2P dateStart: 20230201 isFulltext: true titleUrlDefault: https://search.proquest.com/sciencejournals providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1864-5917 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0062074 issn: 1864-5909 databaseCode: RSV dateStart: 20080301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB7RlgMXWl5iaVn5wA0sNk6c2L2gglohoV2iFlDFJfJjXEDdB7sLh_56xonTFZXohYsvsZ0oY898Hs_MB_DCBGelD5r7HAteSIGccJzkQaF3eeZKq1xLNlFNJur8XNfJ4bZKYZW9TmwVtZ-76CN_nRP2LQgujOSbxU8eWaPi7Wqi0NiCHUI2WQzpGou618SlGLVVmDNVFlzqkU5JM13qnCDjy2OAQrTKml_9bZg2aPPGBWlrd052__eL9-B-QpzsqFsiD-AOzh7Cbs_mwNLmfgR2_HFcHx2yNiWXz-2PThWyqYkJgmyxRB8P6MxcXtBb1t-mjPAuo6UbXRJsemPUnFTRNOV4ssRas3oMn0-OP717zxMDAyc56TXXQbqy8pW3OaLCkJE2UlmGSmhROrQ2uDIIhbnNFZa-pA5I-FMYo4ULweRPYHs2n-FTYJWzWSD0SZayoCOh1NIUEs0IjfAmr6oBZP3vb1wqTx5ZMi6bTWHlKLKGRNa0ImuuBvDyesyiK85xa--DXk5N2qirZiOkAbzqJb15_O_Znt0-2z7ci8T0XXz3AWyvl7_wOdx1v9ffV8sh7Lw9ntSnQ9j6UPFhu2hjW51RW8uv1J6effkDRBD1Rg |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3JbhQxEC2FgAQXwhYxEMAHOIHFtN3utpEiFAFRomSGOQQpt8ZLmUWZhZkBRD6Kb0y5F0ZEIrccOLddLduvys92LQBPbfROhWh4kJjzXAnkxOMUjxqDl5kvnPZ1sYlyONTHx2a0Br-7WJjkVtnZxNpQh6lPd-QvJXHfnOhCX72efeOpalR6Xe1KaDSwOMBfP-nIttjef0vr-0yI3XdHb_Z4W1WA07_NkpuofFGGMjiJqDFmpGE6y1ALIwqPzkVfRKFROqmxCAU1QOJUwlojfIxWktwrcDVPmcWSq6AYdZa_EP0663Omi5wr0zdtkE4Tqidos-fJISKxAMNP_94IV-z23INsvc_tbvxvM3QLbraMmu00KnAb1nByBza6ahWsNV53wQ3eD0Y7r1gdcsyn7mtj6tnYpgBINptjSBcQzJ58olEtP48Z8XlGqpmuXNj4XK8pmdpxG8PK2qo8i3vw4VJGugnrk-kE7wMrvcsisWtiAjkdeZVRNldo-2hFsLIse5B1y135Nv16qgJyUq0SRyeIVASRqoZIddqD53_6zJrkIxe23upwUbWGaFGtQNGDFx2yVp__Le3BxdKewPW9o8Fhdbg_PHgIN2hOZOPLvgXry_l3fATX_I_ll8X8ca0iDD5eNuLOAMIiT4A |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT9wwEB5RqCo4AKVFLI_iQ2_UYuPEic0NASsqYLsStOIW-TEuIPah3cCBX4-dBwuIVqp6jm1ZnvHMN_F8MwBflTOaWyepjTGhCWdIPY7j1Am0Jo5MqoUpm01k3a64vJS9Zyz-Mtu9eZKsOA2hStOg2B1ZtzslvjHvOmlILwg-VdKHdzCXhET6EK-f_2psccraZR3mSKQJ5bIta9rM22u8dE1TvPnqibT0PJ2l_9_zMizWqJPsV2ryEWZwsAJLTUcHUl_wFVh4Vp7wE-izH2e9_T1SknTpUN9UxpH0VaAMktEYbQjZibr9PRxfF1d94hEw8cocflKQ_qtZQ2-c-jXrk9R9bCaf4Wfn6OLgmNY9GaiXnCyodNykmc2sjhEFusjbJxFFKJhkqUGtnUkdExjrWGBqUz8APSJlSklmnFPxKswOhgNcA5IZHTmPR73vTHyQyCVXCUfVRsWsirOsBVEjjtzUBctD34zbfFpqORxp7o80L480f2jBztOcUVWu46-jNxsp5_XVneSxD7ISj0vbvAXfGqlOP_95tfV_G74NH3qHnfz0e_dkA-ZDF_sqGXwTZovxHW7Be3NfXE_GX0qNfgQe0fbl |
| 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=MOMPA%3A+Multi-objective+marine+predator+algorithm+for+solving+multi-objective+optimization+problems&rft.jtitle=Evolutionary+intelligence&rft.au=Jangir%2C+Pradeep&rft.au=Buch%2C+Hitarth&rft.au=Mirjalili%2C+Seyedali&rft.au=Manoharan%2C+Premkumar&rft.date=2023-02-01&rft.pub=Springer+Nature+B.V&rft.issn=1864-5909&rft.eissn=1864-5917&rft.volume=16&rft.issue=1&rft.spage=169&rft.epage=195&rft_id=info:doi/10.1007%2Fs12065-021-00649-z |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1864-5909&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1864-5909&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1864-5909&client=summon |