Grey wolf optimizer: a review of recent variants and applications
Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is requ...
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| Veröffentlicht in: | Neural computing & applications Jg. 30; H. 2; S. 413 - 435 |
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| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
London
Springer London
01.07.2018
Springer Nature B.V |
| Schlagworte: | |
| ISSN: | 0941-0643, 1433-3058 |
| Online-Zugang: | Volltext |
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| Abstract | Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated. |
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| AbstractList | Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated. |
| Author | Al-Betar, Mohammed Azmi Faris, Hossam Aljarah, Ibrahim Mirjalili, Seyedali |
| Author_xml | – sequence: 1 givenname: Hossam surname: Faris fullname: Faris, Hossam organization: Business Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan – sequence: 2 givenname: Ibrahim surname: Aljarah fullname: Aljarah, Ibrahim organization: Business Information Technology Department, King Abdullah II School for Information Technology, The University of Jordan – sequence: 3 givenname: Mohammed Azmi surname: Al-Betar fullname: Al-Betar, Mohammed Azmi organization: Department of Information Technology, Al-Huson University College, Al-Balqa Applied University – sequence: 4 givenname: Seyedali orcidid: 0000-0002-1443-9458 surname: Mirjalili fullname: Mirjalili, Seyedali email: seyedali.mirjalili@griffithuni.edu.au organization: Institute for Integrated and Intelligent Systems, Griffith University |
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| Cites_doi | 10.1108/IJESM-09-2014-0003 10.1007/s10489-014-0645-7 10.1109/ICELMACH.2016.7732627 10.1109/VTCFall.2014.6966100 10.1016/j.enconman.2016.10.062 10.1109/ICITEED.2015.7408911 10.1007/s00521-015-1870-7 10.1109/SOCPAR.2015.7492781 10.1016/j.jocs.2015.03.011 10.1007/s00521-014-1806-7 10.1109/MHS.1995.494215 10.1109/ICEC.1997.592258 10.1002/qre.2107 10.1016/j.procs.2015.09.006 10.1109/TSTE.2015.2482120 10.1109/72.788640 10.1016/j.asoc.2017.03.048 10.1016/j.ijepes.2015.06.005 10.1016/j.soildyn.2015.04.004 10.1016/j.knosys.2015.07.006 10.1155/2016/1205970 10.1063/1.4950945 10.1109/2.485891 10.1049/iet-gtd.2015.0429 10.1080/23311916.2016.1256083 10.1016/j.epsr.2015.12.019 10.1002/9780470512517 10.1007/978-1-4757-2440-0 10.1109/ICEEOT.2016.7755160 10.1109/TEC.2016.2633722 10.1016/j.renene.2017.04.036 10.1887/0750308958 10.1155/2016/7950348 10.1016/j.advengsoft.2013.12.007 10.1063/1.4973255 10.1109/CEC.2016.7744183 10.1109/JSEE.2015.00037 10.1088/1757-899X/83/1/012014 10.1016/j.asoc.2015.09.045 10.1007/978-3-319-26690-9_22 10.1108/COMPEL-09-2015-0337 10.3390/a9010004 10.1109/ICPES.2016.7584086 10.1504/IJETP.2015.069821 10.1016/j.advengsoft.2015.01.010 10.1007/978-981-10-0135-2_13 10.1109/ACCESS.2016.2613940 10.3390/en10040459 10.1109/LPT.2015.2464332 10.1002/cpe.4044 10.1007/s00521-015-1996-7 10.1016/j.advengsoft.2016.05.015 10.1162/NECO_a_00684 10.1007/978-3-642-04944-6_14 10.13189/ujcn.2015.030101 10.1109/ICENCO.2015.7416358 10.1109/4235.585892 10.1007/BF01593790 10.1016/j.ijleo.2016.11.173 10.1109/IJCNN.2015.7280704 10.1080/15325008.2015.1041625 10.15866/iremos.v7i5.2799 10.1007/s00521-015-1962-4 10.1007/s40313-017-0305-3 10.1023/A:1008202821328 10.1016/j.ijepes.2015.07.031 10.1007/978-3-319-47054-2_25 10.1109/ICSECS.2015.7333107 10.1016/j.asoc.2015.03.041 10.1109/ICACCI.2016.7732343 10.1109/ICRITO.2015.7359368 10.1109/ICACCI.2015.7275611 10.1016/j.ifacol.2016.07.089 10.1080/15325008.2017.1292567 10.1007/s00521-015-1920-1 10.1007/978-3-319-26690-9_27 10.1016/j.eswa.2017.04.029 10.1080/1448837X.2015.1092933 10.1109/ACCESS.2017.2726586 10.1049/iet-gtd.2015.1141 10.1007/978-3-319-26690-9_21 10.1016/j.ifacol.2016.03.151 10.1515/jisys-2014-0137 10.1007/s00521-016-2644-6 10.5220/0006048201710177 10.1007/978-981-10-0448-3_87 10.1109/ICCIC.2015.7435714 10.1109/ICSEC.2014.6978196 10.1007/s11042-016-4312-3 10.1109/TIE.2016.2607698 10.1504/IJDMB.2016.078151 10.1016/j.neucom.2015.06.083 10.1016/j.eswa.2015.10.039 10.1016/j.energy.2016.05.105 10.1109/ICIEV.2015.7334054 10.1109/ICSCTI.2015.7489575 10.1109/ISCBI.2016.7743266 10.1016/j.advengsoft.2016.06.004 10.1016/j.knosys.2015.12.022 10.1109/TEVC.2008.919004 10.1080/23311916.2016.1151612 10.1016/j.enconman.2015.04.005 10.1016/j.energy.2016.05.128 10.1049/iet-gtd.2015.0726 10.1016/j.supflu.2016.04.006 10.1109/PECON.2014.7062431 10.1145/2480741.2480752 10.1016/j.procs.2016.07.329 10.1007/978-3-540-30217-9_29 10.1016/j.atmosenv.2016.03.056 10.1109/ICCKE.2015.7365818 10.1016/j.ijepes.2016.04.034 10.14311/NNW.2016.26.023 10.1155/2017/9512741 10.1016/j.advengsoft.2016.01.008 10.1016/j.asoc.2016.12.022 10.1016/j.cnsns.2012.05.010 10.1016/j.engappai.2016.10.013 |
| ContentType | Journal Article |
| Copyright | The Natural Computing Applications Forum 2017 Copyright Springer Science & Business Media 2018 |
| Copyright_xml | – notice: The Natural Computing Applications Forum 2017 – notice: Copyright Springer Science & Business Media 2018 |
| DBID | AAYXX CITATION |
| DOI | 10.1007/s00521-017-3272-5 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1433-3058 |
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| References | Muangkote N, Sunat K, Chiewchanwattana S (2014) An improved grey wolf optimizer for training q-gaussian radial basis functional-link nets. In: 2014 international computer science and engineering conference (ICSEC), pp 209–214 LiSXWangJSDynamic modeling of steam condenser and design of pi controller based on grey wolf optimizerMath Probl Eng201520159 MirjaliliSHow effective is the grey wolf optimizer in training multi-layer perceptronsAppl Intell201543115016110.1007/s10489-014-0645-7 MahdadBSrairiKBlackout risk prevention in a smart grid based flexible optimal strategy using grey wolf-pattern search algorithmsEnergy Convers Manag20159841142910.1016/j.enconman.2015.04.005 Mostafa A, Fouad A, Houseni M, Allam N, Hassanien AE, Hefny H, Aslanishvili I (2016) A hybrid grey wolf based segmentation with statistical image for ct liver images. In: International conference on advanced intelligent systems and informatics. Springer, pp 846–855 KozaJRGenetic programming: on the programming of computers by means of natural selection1992CambridgeMIT Press0850.68161 Chaman-MotlaghASuperdefect photonic crystal filter optimization using grey wolf optimizerIEEE Photonics Technol Lett201527222355235810.1109/LPT.2015.2464332 MohantySSubudhiBRayPKA grey wolf-assisted perturb observe MPPT algorithm for a PV systemIEEE Trans Energy Convers20163234034710.1109/TEC.2016.2633722 Sangwan V, Kumar R, Rathore AK (2016) Estimation of battery parameters of the equivalent circuit model using grey wolf optimization. In: 2016 IEEE 6th international conference on power systems (ICPS). IEEE, pp 1–6 EmaryEZawbaaHMGrosanCHassenianAEFeature subset selection approach by gray-wolf optimization2015ChamSpringer113 KomakiGMKayvanfarVGrey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release timeJ Comput Sci2015810912010.1016/j.jocs.2015.03.011 GandomiAHAlaviAHKrill herd: a new bio-inspired optimization algorithmCommun Nonlinear Sci Numer Simul2012171248314845296027910.1016/j.cnsns.2012.05.0101266.65092 SanjayRJayabarathiTRaghunathanTRameshVMithulananthanNOptimal allocation of distributed generation using hybrid grey wolf optimizerIEEE Access20175148071481810.1109/ACCESS.2017.2726586 MirjaliliSThe ant lion optimizerAdv Eng Softw201583809810.1016/j.advengsoft.2015.01.010 KhairuzzamanAKMChaudhurySMultilevel thresholding using grey wolf optimizer for image segmentationExpert Syst Appl201786647610.1016/j.eswa.2017.04.029 Sweidan AH, El-Bendary N, Hassanien AE, Hegazy OM, Mohamed A-K (2016) Grey wolf optimizer and case-based reasoning model for water quality assessment. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28–30, 2015, Beni Suef, Egypt. Springer, pp. 229–239 Elhariri E, El-Bendary N, Hassanien AE, Abraham A (2015) Grey wolf optimization for one-against-one multi-class support vector machines. In: 2015 7th international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 7–12 SongXTangLZhaoSZhangXLiLHuangJCaiWGrey wolf optimizer for parameter estimation in surface wavesSoil Dyn Earthq Eng20157514715710.1016/j.soildyn.2015.04.004 LiLSunLKangWGuoJChongHLiSFuzzy multilevel image thresholding based on modified discrete grey wolf optimizer and local information aggregationIEEE Access201646438645010.1109/ACCESS.2016.2613940 LiLSunLGuoJQiJXuBLiSModified discrete grey wolf optimizer algorithm for multilevel image thresholdingComput Intell Neurosci2017201716 EngelbrechtAPComputational intelligence: an introduction2007HobokenWiley10.1002/9780470512517 SulaimanMHMustaffaZMohamedMRAlimanOUsing the gray wolf optimizer for solving optimal reactive power dispatch problemAppl Soft Comput20153228629210.1016/j.asoc.2015.03.041 Al-Aboody NA, Al-Raweshidy HS (2016) Grey wolf optimization-based energy-efficient routing protocol for heterogeneous wireless sensor networks. In: 2016 4th international symposium on computational and business intelligence (ISCBI). IEEE, pp 101–107 DorigoMGambardellaLMAnt colony system: a cooperative learning approach to the traveling salesman problemIEEE Trans Evol Comput199711536610.1109/4235.585892 MittalNSinghUSohiBSModified grey wolf optimizer for global engineering optimizationAppl Comput Intell Soft Comput20162016810.1155/2016/7950348 Hai Phong Private Universty (2016) Estimation localization in wireless sensor network based on multi-objective grey wolf optimizer. In: Advances in information and communication technology: proceedings of the international conference, ICTA 2016, vol 538. Springer, p 228 ČrepinšekMLiuS-HMernikMExploration and exploitation in evolutionary algorithms: a surveyACM Comput Surv (CSUR)2013453351293.68251 JayapriyaJArockMAligning two molecular sequences using genetic operators in grey wolf optimiser techniqueInt J Data Min Bioinf201615432834910.1504/IJDMB.2016.078151 RazmjooyNRamezaniMNamadchianAA new lqr optimal control for a single-link flexible joint robot manipulator based on grey wolf optimizerMajlesi J Electr Eng201610353 EmaryEZawbaaHMHassanienAEBinary grey wolf optimization approaches for feature selectionNeurocomputing201617237138110.1016/j.neucom.2015.06.083 RameshkumarJGanesanSAbiramiMSubramanianSCost, emission and reserve pondered pre-dispatch of thermal power generating units coordinated with real coded grey wolf optimisationIET Gener Trans Distrib201610497298510.1049/iet-gtd.2015.0726 ZhouJZhuWZhengYLiCPrecise equivalent model of small hydro generator cluster and its parameter identification using improved grey wolf optimiserIET Gener Transm Distrib20161092108211710.1049/iet-gtd.2015.1141 KumarAPantSRamMSystem reliability optimization using gray wolf optimizer algorithmQual Reliab Eng Int2016331327133510.1002/qre.2107 Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS’95. IEEE, pp 39–43 StornRPriceKDifferential evolution-a simple and efficient heuristic for global optimization over continuous spacesJ Global Optim1997114341359147955310.1023/A:10082028213280888.90135 PrecupREDavidRCPetriuEMSzedlak-StineanAIClaudia-AdinaB-DGrey wolf optimizer-based approach to the tuning of pi-fuzzy controllers with a reduced process parametric sensitivityIFAC-PapersOnLine2016495556010.1016/j.ifacol.2016.07.089 Wong LI, Sulaiman MH, Mohamed MR, Hong MS (2014) Grey wolf optimizer for solving economic dispatch problems. In: 2014 IEEE international conference on power and energy (PECon). IEEE, pp 150–154 KambojVKA novel hybrid pso-gwo approach for unit commitment problemNeural Comput Appl20162761643165510.1007/s00521-015-1962-4 Korayem L, Khorsid M, Kassem SS (2015) Using grey wolf algorithm to solve the capacitated vehicle routing problem. In: IOP conference series: materials science and engineering, vol 83. IOP Publishing, p 012014 ZhangSZhouYLiZPanWGrey wolf optimizer for unmanned combat aerial vehicle path planningAdv Eng Softw20169912113610.1016/j.advengsoft.2016.05.015 Hansen N, Kern S (2004) Evaluating the CMA evolution strategy on multimodal test functions. In: International conference on parallel problem solving from nature. Springer, pp 282–291 JayakumarNSubramanianSGanesanSElanchezhianEBCombined heat and power dispatch by grey wolf optimizationInt J Energy Sect Manag20159452354610.1108/IJESM-09-2014-0003 RodríguezLCastilloOSoriaJMelinPValdezFGonzalezCIMartinezGESotoJA fuzzy hierarchical operator in the grey wolf optimizer algorithmAppl Soft Comput20175731532810.1016/j.asoc.2017.03.048 SaxenaPKothariAOptimal pattern synthesis of linear antenna array using grey wolf optimization algorithmInt J Antennas Propag201620161110.1155/2016/1205970 VermaSKYadavSNagarSKOptimization of fractional order pid controller using grey wolf optimizerJ Control Autom Electr Syst2017281910.1007/s40313-017-0305-3 FathyAAbdelazizAYGrey wolf optimizer for optimal sizing and siting of energy storage system in electric distribution networkElectric Power Compon Syst20174511410.1080/15325008.2017.1292567 AliMElhameedMAFarahatMAEffective parameters identification for polymer electrolyte membrane fuel cell models using grey wolf optimizerRenew Energy201711145546210.1016/j.renene.2017.04.036 EngelbrechtAPFundamentals of computational swarm intelligence2006HobokenWiley NiuMWangYSunSLiYA novel hybrid decomposition-and-ensemble model based on ceemd and gwo for short-term pm 2.5 concentration forecastingAtmos Environ201613416818010.1016/j.atmosenv.2016.03.056 YadavSVermaSKNagarSKOptimized pid controller for magnetic levitation systemIFAC-PapersOnLine201649177878210.1016/j.ifacol.2016.03.151 Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214 MadadiAMotlaghMMOptimal control of dc motor using grey wolf optimizer algorithmTJEAS Journal-2014-4-04/373-37920144437379 VapnikVThe nature of statistical learning theory1995New YorkSpringer10.1007/978-1-4757-2440-00833.62008 PrecupREDavidRCPetriuEMGrey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivityIEEE Trans Industr Electron201764152753410.1109/TIE.2016.2607698 Vosooghifard M, Ebrahimpour H (2015) Applying grey wolf optimizer-based decision tree classifer for cancer classification on gene expression data. In: 2015 5th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 147–151 Tsai PW, Dao TK, et al (2016) Robot path planning optimization based on multiobjective grey wolf optimizer. In: International conference on genetic and evolutionary computing. Springer, pp 166–173 Dao TK (2016) Enhanced diversity herds grey wolf optimizer for optimal area coverage in wireless sensor networks. In: Genetic and evolutionary computing: proceedings of the tenth international conference on genetic and evolutionary computing, November 7–9, 2016 Fuzhou City, Fujian Province, China, vol 536. Springer, p 174 DudaniARChudasamaKPartial discharge detection in transformer using ad J Jayapriya (3272_CR46) 2016; 15 M Niu (3272_CR95) 2016; 134 AP Engelbrecht (3272_CR23) 2006 S Mirjalili (3272_CR84) 2016; 47 S Mirjalili (3272_CR76) 2015; 83 S Mirjalili (3272_CR81) 2016; 95 W Long (3272_CR67) 2016; 28 VK Kamboj (3272_CR49) 2015; 27 YL Karnavas (3272_CR52) 2016; 6 3272_CR45 Q Luo (3272_CR70) 2015; 9 3272_CR124 3272_CR47 RE Precup (3272_CR100) 2017; 64 S Mirjalili (3272_CR82) 2016; 27 M Črepinšek (3272_CR8) 2013; 45 MR Mosavi (3272_CR90) 2016; 26 3272_CR125 E Gupta (3272_CR32) 2016; 3 A Kumar (3272_CR60) 2016; 33 3272_CR129 3272_CR50 3272_CR51 S Saremi (3272_CR111) 2015; 26 D Simon (3272_CR116) 2008; 12 3272_CR131 J Rameshkumar (3272_CR103) 2016; 10 3272_CR133 3272_CR56 3272_CR58 AR Dudani (3272_CR14) 2016; 3 J Rameshkumar (3272_CR102) 2015; 13 S Mohanty (3272_CR88) 2016; 32 N Razmjooy (3272_CR104) 2016; 10 H Yang (3272_CR136) 2015; 5 MR Shakarami (3272_CR113) 2016; 133 S Eswaramoorthy (3272_CR25) 2016; 35 AA El-Fergany (3272_CR16) 2015; 43 SX Li (3272_CR66) 2015; 2015 V Kumar (3272_CR61) 2017; 26 S Zhang (3272_CR141) 2016; 99 AP Engelbrecht (3272_CR24) 2007 3272_CR26 B Mahdad (3272_CR72) 2015; 98 N Jayakumar (3272_CR43) 2015; 9 S Mirjalili (3272_CR78) 2015; 89 3272_CR28 S Zhang (3272_CR139) 2015; 2015 S Sharma (3272_CR114) 2016; 10 A Madadi (3272_CR71) 2014; 4 L Rodríguez (3272_CR107) 2017; 57 3272_CR106 3272_CR105 3272_CR109 L Li (3272_CR63) 2017; 2017 3272_CR30 M Pradhan (3272_CR99) 2016; 83 HM Song (3272_CR118) 2014; 7 3272_CR33 3272_CR34 A Zhu (3272_CR143) 2015; 26 3272_CR35 C Lu (3272_CR69) 2016; 99 3272_CR37 DK Lal (3272_CR62) 2016; 92 B Yang (3272_CR134) 2016; 133 3272_CR38 X Song (3272_CR119) 2015; 75 3272_CR39 U Sultana (3272_CR123) 2016; 111 A Sahoo (3272_CR108) 2017; 52 A Chaman-Motlagh (3272_CR6) 2015; 27 S Yadav (3272_CR132) 2016; 49 N Jayakumar (3272_CR44) 2016; 74 P Berkhin (3272_CR4) 2006 M Ali (3272_CR2) 2017; 111 E Emary (3272_CR22) 2016; 172 AAM El-Gaafary (3272_CR17) 2015; 3 MJD Powell (3272_CR98) 1977; 12 A Noshadi (3272_CR96) 2016; 27 S Zhang (3272_CR140) 2017; 130 E Emary (3272_CR20) 2015; 65 K Sujatha (3272_CR121) 2017 N Mittal (3272_CR85) 2016; 2016 L Wang (3272_CR130) 2017; 10 3272_CR86 Y Sharma (3272_CR115) 2015; 73 3272_CR89 S Mohanty (3272_CR87) 2016; 7 S Mirjalili (3272_CR83) 2014; 69 AH Gandomi (3272_CR29) 2012; 17 A Khalili (3272_CR54) 2017 G Sodeifian (3272_CR117) 2016; 114 AK Jain (3272_CR40) 1996; 29 SA Medjahed (3272_CR75) 2016; 40 S Mirjalili (3272_CR79) 2016; 27 S Mirjalili (3272_CR77) 2015; 43 V Vapnik (3272_CR127) 1999; 5 M Dorigo (3272_CR13) 1997; 1 N Jayakumar (3272_CR42) 2015; 11 3272_CR91 3272_CR92 L Li (3272_CR64) 2016; 4 3272_CR93 3272_CR94 GM Komaki (3272_CR57) 2015; 8 3272_CR97 3272_CR10 3272_CR11 MH Sulaiman (3272_CR122) 2015; 32 3272_CR15 3272_CR18 JR Koza (3272_CR59) 1992 3272_CR19 EM Devi (3272_CR12) 2017; 7 3272_CR1 R Storn (3272_CR120) 1997; 11 3272_CR137 A Fathy (3272_CR27) 2017; 45 MJ Hadidian-Moghaddam (3272_CR36) 2016; 8 3272_CR138 S Gholizadeh (3272_CR31) 2015; 5 T Jayabarathi (3272_CR41) 2016; 111 R Sanjay (3272_CR110) 2017; 5 B Yang (3272_CR135) 2017; 133 V Vapnik (3272_CR126) 1995 P Saxena (3272_CR112) 2016; 2016 T Bäck (3272_CR3) 1997 J Zhou (3272_CR142) 2016; 10 C Lu (3272_CR68) 2017; 57 S Mirjalili (3272_CR80) 2016; 96 E Emary (3272_CR21) 2015 Q Li (3272_CR65) 2017; 2017 S Khalilpourazari (3272_CR55) 2016 AKM Khairuzzaman (3272_CR53) 2017; 86 3272_CR73 3272_CR74 VK Kamboj (3272_CR48) 2016; 27 3272_CR7 SK Verma (3272_CR128) 2017; 28 3272_CR5 RE Precup (3272_CR101) 2016; 49 3272_CR9 |
| References_xml | – reference: EswaramoorthySSivakumaranNSekaranSGrey wolf optimization based parameter selection for support vector machinesCOMPEL-The Int J Comput Math Electr Electron Eng20163551513152310.1108/COMPEL-09-2015-0337 – reference: LiLSunLGuoJQiJXuBLiSModified discrete grey wolf optimizer algorithm for multilevel image thresholdingComput Intell Neurosci2017201716 – reference: ZhuAXuCLiZWuJLiuZHybridizing grey wolf optimization with differential evolution for global optimization and test scheduling for 3d stacked socJ Syst Eng Electron201526231732810.1109/JSEE.2015.00037 – reference: KhairuzzamanAKMChaudhurySMultilevel thresholding using grey wolf optimizer for image segmentationExpert Syst Appl201786647610.1016/j.eswa.2017.04.029 – reference: Malik MRS, Mohideen ER, Ali L (2015) Weighted distance grey wolf optimizer for global optimization problems. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC). IEEE, pp 1–6 – reference: NoshadiAShiJLeeWSShiPKalamAOptimal pid-type fuzzy logic controller for a multi-input multi-output active magnetic bearing systemNeural Comput Appl20162772031204610.1007/s00521-015-1996-7 – reference: Karnavas YL, Chasiotis ID (2016) PMDC coreless micro-motor parameters estimation through grey wolf optimizer. In: 2016 XXII international conference on electrical machines (ICEM). IEEE, pp 865–870 – reference: MohantySSubudhiBRayPKA grey wolf-assisted perturb observe MPPT algorithm for a PV systemIEEE Trans Energy Convers20163234034710.1109/TEC.2016.2633722 – reference: Malik MRS, Mohideen ER, Ali L, Raziuddin S (2016) Weighted distance grey wolf optimizer to control air pollution of delhi thermal power plant. J Ind Pollut Control 32(1). http://www.icontrolpollution.com/articles/weighted-distance-grey-wolf-optimizer-to-control-air-pollution-of-delhi-thermal-power-plant-.php?aid=75885 – reference: LalDKBarisalAKTripathyMGrey wolf optimizer algorithm based fuzzy PID controller for AGC of multi-area power system with TCPsProcedia Comput Sci2016929910510.1016/j.procs.2016.07.329 – reference: Yamany W, Emary E, Hassanien AE (2016) New rough set attribute reduction algorithm based on grey wolf optimization. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28–30, 2015, Beni Suef, Egypt. Springer, pp 241–251 – reference: NiuMWangYSunSLiYA novel hybrid decomposition-and-ensemble model based on ceemd and gwo for short-term pm 2.5 concentration forecastingAtmos Environ201613416818010.1016/j.atmosenv.2016.03.056 – reference: Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, 1995. MHS’95. IEEE, pp 39–43 – reference: RodríguezLCastilloOSoriaJMelinPValdezFGonzalezCIMartinezGESotoJA fuzzy hierarchical operator in the grey wolf optimizer algorithmAppl Soft Comput20175731532810.1016/j.asoc.2017.03.048 – reference: LuoQZhangSLiZZhouYA novel complex-valued encoding grey wolf optimization algorithmAlgorithms2015914349074710.3390/a9010004 – reference: MirjaliliSLewisAThe whale optimization algorithmAdv Eng Softw201695516710.1016/j.advengsoft.2016.01.008 – reference: SharmaYSaikiaLCAutomatic generation control of a multi-area ST thermal power system using grey wolf optimizer algorithm based classical controllersInt J Electr Power Energy Syst20157385386210.1016/j.ijepes.2015.06.005 – reference: VapnikVAn overview of statistical learning theoryIEEE Trans Neural Netw1999598899910.1109/72.788640 – reference: Ghazzai H, Yaacoub E, Alouini MS (2014) Optimized lte cell planning for multiple user density subareas using meta-heuristic algorithms. In: 2014 IEEE 80th vehicular technology conference (VTC2014-Fall). IEEE, pp 1–6 – reference: LongWLiangXCaiSJiaoJZhangWA modified augmented Lagrangian with improved grey wolf optimization to constrained optimization problemsNeural Comput Appl201628Suppl 1S421S438 – reference: DorigoMGambardellaLMAnt colony system: a cooperative learning approach to the traveling salesman problemIEEE Trans Evol Comput199711536610.1109/4235.585892 – reference: Fouad MM, Hafez AI, Hassanien AE, Snasel V (2015) Grey wolves optimizer-based localization approach in WSNs. In: 2015 11th international computer engineering conference (ICENCO). IEEE, pp 256–260 – reference: Das KR, Das D, Das J (2015) Optimal tuning of pid controller using gwo algorithm for speed control in dc motor. In: 2015 international conference on soft computing techniques and implementations (ICSCTI). IEEE, pp 108–112 – reference: SongXTangLZhaoSZhangXLiLHuangJCaiWGrey wolf optimizer for parameter estimation in surface wavesSoil Dyn Earthq Eng20157514715710.1016/j.soildyn.2015.04.004 – reference: PrecupREDavidRCPetriuEMSzedlak-StineanAIClaudia-AdinaB-DGrey wolf optimizer-based approach to the tuning of pi-fuzzy controllers with a reduced process parametric sensitivityIFAC-PapersOnLine2016495556010.1016/j.ifacol.2016.07.089 – reference: JayapriyaJArockMAligning two molecular sequences using genetic operators in grey wolf optimiser techniqueInt J Data Min Bioinf201615432834910.1504/IJDMB.2016.078151 – reference: Korayem L, Khorsid M, Kassem SS (2015) Using grey wolf algorithm to solve the capacitated vehicle routing problem. In: IOP conference series: materials science and engineering, vol 83. IOP Publishing, p 012014 – reference: Faris H, Aljarah I, Mirjalili S, Castillo PA, Merelo JJ (2016) Evolopy: an open-source nature-inspired optimization framework in python. In: IJCCI 2016—proceedings of the 8th International joint conference on computational intelligence, vol 1, pp 171–177 – reference: Yang X-S, Deb S (2009) Cuckoo search via lévy flights. In: World congress on nature & biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214 – reference: Hadidian-MoghaddamMJArabi-NowdehSBigdeliMOptimal sizing of a stand-alone hybrid photovoltaic/wind system using new grey wolf optimizer considering reliabilityJ Renew Sustain Energy20168303590310.1063/1.4950945 – reference: Bhensdadia V, Tejani G (2016) Grey wolf optimizer (GWO) algorithm for minimum weight planer frame design subjected to AISC-LRFD. In: Proceedings of international conference on ICT for sustainable development. Springer, pp 143–151 – reference: MirjaliliSSca: a sine cosine algorithm for solving optimization problemsKnowl-Based Syst20169612013310.1016/j.knosys.2015.12.022 – reference: MirjaliliSMirjaliliSMLewisAGrey wolf optimizerAdv Eng Softw201469466110.1016/j.advengsoft.2013.12.007 – reference: KhaliliABabamirSMOptimal scheduling workflows in cloud computing environment using pareto-based grey wolf optimizerPract Exp Concurr Comput2017 – reference: JayakumarNSubramanianSElanchezhianEBGanesanSAn application of grey wolf optimisation for combined heat and power dispatchInt J Energy Technol Policy201511218320610.1504/IJETP.2015.069821 – reference: El-GaafaryAAMMohamedYSHemeidaAMMohamedAAAGrey wolf optimization for multi input multi output systemUniv J Commun Netw2015311610.13189/ujcn.2015.030101 – reference: ShakaramiMRDavoudkhaniIFWide-area power system stabilizer design based on grey wolf optimization algorithm considering the time delayElectr Power Syst Res201613314915910.1016/j.epsr.2015.12.019 – reference: Elhariri E, El-Bendary N, Hassanien AE (2016) A hybrid classification model for EMG signals using grey wolf optimizer and SVMs. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28–30, 2015, Beni Suef, Egypt. Springer, pp 297–307 – reference: MittalNSinghUSohiBSModified grey wolf optimizer for global engineering optimizationAppl Comput Intell Soft Comput20162016810.1155/2016/7950348 – reference: Gupta P, Kumar V, Rana KPS, Mishra P (2015) Comparative study of some optimization techniques applied to jacketed cstr control. In: 015 4th international conference on reliability, infocom technologies and optimization (ICRITO)(trends and future directions). IEEE, pp 1–6 – reference: Mustaffa Z, Sulaiman MH, Kahar MNM (2015) LS-SVM hyper-parameters optimization based on GWO algorithm for time series forecasting. In: 2015 4th international conference on software engineering and computer systems (ICSECS). IEEE, pp 183–188 – reference: KambojVKA novel hybrid pso-gwo approach for unit commitment problemNeural Comput Appl20162761643165510.1007/s00521-015-1962-4 – reference: YangHLiuJA hybrid clustering algorithm based on grey wolf optimizer and k-means algorithmJ Jiangxi Univ Sci Technol20155015 – reference: YangBZhangXYuTShuHZihaoFGrouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbineEnergy Convers Manag201713342744310.1016/j.enconman.2016.10.062 – reference: Gupta S, Deep K, Chamola BP, Kumari P (2017) Performance of grey wolf optimizer on large scale problems. In: AIP conference proceedings, vol 1802. AIP Publishing, p 020005 – reference: KumarAPantSRamMSystem reliability optimization using gray wolf optimizer algorithmQual Reliab Eng Int2016331327133510.1002/qre.2107 – reference: Rodríguez L, Castillo O, Soria J (2016) Grey wolf optimizer with dynamic adaptation of parameters using fuzzy logic. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 3116–3123 – reference: JayakumarNSubramanianSGanesanSElanchezhianEBCombined heat and power dispatch by grey wolf optimizationInt J Energy Sect Manag20159452354610.1108/IJESM-09-2014-0003 – reference: Höhfeld M, Rudolph G (1997) Towards a theory of population-based incremental learning. In In: Proceedings of the 4th IEEE conference on evolutionary computation. Citeseer – reference: SujathaKPunithavathaniDSOptimized ensemble decision-based multi-focus imagefusion using binary genetic grey-wolf optimizer in camera sensor networksMultimed Tools Appl2017 – reference: Kishor A, Singh, PK (2016) Empirical study of grey wolf optimizer. In: Proceedings of fifth international conference on soft computing for problem solving. Springer, pp 1037–1049 – reference: LiLSunLKangWGuoJChongHLiSFuzzy multilevel image thresholding based on modified discrete grey wolf optimizer and local information aggregationIEEE Access201646438645010.1109/ACCESS.2016.2613940 – reference: ZhangSZhouYLiZPanWGrey wolf optimizer for unmanned combat aerial vehicle path planningAdv Eng Softw20169912113610.1016/j.advengsoft.2016.05.015 – reference: Elhariri E, El-Bendary N, Hassanien AE, Abraham A (2015) Grey wolf optimization for one-against-one multi-class support vector machines. In: 2015 7th international conference of soft computing and pattern recognition (SoCPaR). IEEE, pp 7–12 – reference: MosaviMRKhisheMGhamgosarAClassification of sonar data set using neural network trained by gray wolf optimizationNeural Netw World201626439310.14311/NNW.2016.26.023 – reference: MirjaliliSHow effective is the grey wolf optimizer in training multi-layer perceptronsAppl Intell201543115016110.1007/s10489-014-0645-7 – reference: SongHMSulaimanMHMohamedMRAn application of grey wolf optimizer for solving combined economic emission dispatch problemsInt Rev Model Simul (IREMOS)20147583884410.15866/iremos.v7i5.2799 – reference: BerkhinPA survey of clustering data mining techniques2006BerlinSpringer2571 – reference: DudaniARChudasamaKPartial discharge detection in transformer using adaptive grey wolf optimizer based acoustic emission techniqueCogent Eng201631125608310.1080/23311916.2016.1256083 – reference: Gupta P, Rana KPS, Kumar V, Mishra P, Kumar J, Nair SS (2015) Development of a grey wolf optimizer toolkit in labview. In: 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE). IEEE, pp 107–113 – reference: RameshkumarJGanesanSSubramanianSAbiramiMShort-term unit consignment solution using real-coded grey wolf algorithmAust J Electr Electron Eng2015131546610.1080/1448837X.2015.1092933 – reference: YadavSVermaSKNagarSKOptimized pid controller for magnetic levitation systemIFAC-PapersOnLine201649177878210.1016/j.ifacol.2016.03.151 – reference: KhalilpourazariSKhalilpourazarySOptimization of production time in the multi-pass milling process via a robust grey wolf optimizerNeural Comput Appl2016 – reference: PradhanMRoyPKPalTGrey wolf optimization applied to economic load dispatch problemsInt J Electr Power Energy Syst20168332533410.1016/j.ijepes.2016.04.034 – reference: VapnikVThe nature of statistical learning theory1995New YorkSpringer10.1007/978-1-4757-2440-00833.62008 – reference: ZhouJZhuWZhengYLiCPrecise equivalent model of small hydro generator cluster and its parameter identification using improved grey wolf optimiserIET Gener Transm Distrib20161092108211710.1049/iet-gtd.2015.1141 – reference: GandomiAHAlaviAHKrill herd: a new bio-inspired optimization algorithmCommun Nonlinear Sci Numer Simul2012171248314845296027910.1016/j.cnsns.2012.05.0101266.65092 – reference: PrecupREDavidRCPetriuEMGrey wolf optimizer algorithm-based tuning of fuzzy control systems with reduced parametric sensitivityIEEE Trans Industr Electron201764152753410.1109/TIE.2016.2607698 – reference: Hai Phong Private Universty (2016) Estimation localization in wireless sensor network based on multi-objective grey wolf optimizer. In: Advances in information and communication technology: proceedings of the international conference, ICTA 2016, vol 538. Springer, p 228 – reference: Davis L (ed) (1991) Handbook of genetic algorithms. Van Nostrand Reinhold, New York – reference: Jitkongchuen D (2015) A hybrid differential evolution with grey wolf optimizer for continuous global optimization. In: 2015 7th international conference on information technology and electrical engineering (ICITEE). IEEE, pp 51–54 – reference: JayabarathiTRaghunathanTAdarshBRSuganthanPNEconomic dispatch using hybrid grey wolf optimizerEnergy201611163064110.1016/j.energy.2016.05.105 – reference: GholizadehSOptimal design of double layer grids considering nonlinear behaviour by sequential grey wolf algorithmJ Optim Civ Eng201554511523 – reference: SimonDBiogeography-based optimizationIEEE Trans Evol Comput200812670271310.1109/TEVC.2008.919004 – reference: Vosooghifard M, Ebrahimpour H (2015) Applying grey wolf optimizer-based decision tree classifer for cancer classification on gene expression data. In: 2015 5th international conference on computer and knowledge engineering (ICCKE). IEEE, pp 147–151 – reference: KozaJRGenetic programming: on the programming of computers by means of natural selection1992CambridgeMIT Press0850.68161 – reference: Pan TS, Dao TK, Chu SC, et al (2015) A communication strategy for paralleling grey wolf optimizer. In: International conference on genetic and evolutionary computing. Springer, pp 253–262 – reference: SahooAChandraSMulti-objective grey wolf optimizer for improved cervix lesion classificationAppl Soft Comput201752648010.1016/j.asoc.2016.12.022 – reference: RazmjooyNRamezaniMNamadchianAA new lqr optimal control for a single-link flexible joint robot manipulator based on grey wolf optimizerMajlesi J Electr Eng201610353 – reference: MahdadBSrairiKBlackout risk prevention in a smart grid based flexible optimal strategy using grey wolf-pattern search algorithmsEnergy Convers Manag20159841142910.1016/j.enconman.2015.04.005 – reference: MirjaliliSDragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problemsNeural Comput Appl20162741053107310.1007/s00521-015-1920-1 – reference: JainAKMaoJMohiuddinKMartificial neural networks: a tutorialComputer1996293314410.1109/2.485891 – reference: El-FerganyAAHasanienHMSingle and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithmsElectric Power Compon Syst201543131548155910.1080/15325008.2015.1041625 – reference: Mustaffa Z, Sulaiman MH, Kahar MNM (2015) Training lssvm with gwo for price forecasting. In: 2015 international conference on informatics, electronics & vision (ICIEV). IEEE, pp 1–6 – reference: Sweidan AH, El-Bendary N, Hassanien AE, Hegazy OM, Mohamed A-K (2016) Grey wolf optimizer and case-based reasoning model for water quality assessment. In: The 1st international conference on advanced intelligent system and informatics (AISI2015), November 28–30, 2015, Beni Suef, Egypt. Springer, pp. 229–239 – reference: MirjaliliSThe ant lion optimizerAdv Eng Softw201583809810.1016/j.advengsoft.2015.01.010 – reference: Tsai PW, Dao TK, et al (2016) Robot path planning optimization based on multiobjective grey wolf optimizer. In: International conference on genetic and evolutionary computing. Springer, pp 166–173 – reference: SaxenaPKothariAOptimal pattern synthesis of linear antenna array using grey wolf optimization algorithmInt J Antennas Propag201620161110.1155/2016/1205970 – reference: LuCXiaoSLiXGaoLAn effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding productionAdv Eng Softw20169916117610.1016/j.advengsoft.2016.06.004 – reference: EmaryEZawbaaHMHassanienAEBinary grey wolf optimization approaches for feature selectionNeurocomputing201617237138110.1016/j.neucom.2015.06.083 – reference: LiQChenHHuangHZhaoXCaiZNTongCLiuWTianXAn enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosisComput Math Methods Med2017201715 – reference: SanjayRJayabarathiTRaghunathanTRameshVMithulananthanNOptimal allocation of distributed generation using hybrid grey wolf optimizerIEEE Access20175148071481810.1109/ACCESS.2017.2726586 – reference: Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report, technical report-tr06, Erciyes University, engineering faculty, computer engineering department, – reference: Chandra M, Agrawal A, Kishor A, Niyogi R (2016) Web service selection with global constraints using modified gray wolf optimizer. In: 2016 international conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 1989–1994 – reference: KomakiGMKayvanfarVGrey wolf optimizer algorithm for the two-stage assembly flow shop scheduling problem with release timeJ Comput Sci2015810912010.1016/j.jocs.2015.03.011 – reference: SodeifianGArdestaniNSSajadianSAGhorbandoostSApplication of supercritical carbon dioxide to extract essential oil from cleome coluteoides boiss: Experimental, response surface and grey wolf optimization methodologyJ Supercrit Fluids2016114556310.1016/j.supflu.2016.04.006 – reference: Jayapriya J, Arock M (2015) A parallel gwo technique for aligning multiple molecular sequences. In: 2015 International conference on advances in computing, communications and informatics (ICACCI). IEEE, pp 210–215 – reference: JayakumarNSubramanianSGanesanSElanchezhianEBGrey wolf optimization for combined heat and power dispatch with cogeneration systemsInt J Electr Power Energy Syst20167425226410.1016/j.ijepes.2015.07.031 – reference: Wong LI, Sulaiman MH, Mohamed MR, Hong MS (2014) Grey wolf optimizer for solving economic dispatch problems. In: 2014 IEEE international conference on power and energy (PECon). IEEE, pp 150–154 – reference: DeviEMSugantheRCFeature selection in intrusion detection grey wolf optimizerAsian J Res Soc Sci Hum201773671682 – reference: FathyAAbdelazizAYGrey wolf optimizer for optimal sizing and siting of energy storage system in electric distribution networkElectric Power Compon Syst20174511410.1080/15325008.2017.1292567 – reference: Mohamed AAA, El-Gaafary AAM, Mohamed YS, Hemeida AM (2015) Design static var compensator controller using artificial neural network optimized by modify grey wolf optimization. In: 2015 international joint conference on neural networks (IJCNN). IEEE, pp 1–7 – reference: Al-Aboody NA, Al-Raweshidy HS (2016) Grey wolf optimization-based energy-efficient routing protocol for heterogeneous wireless sensor networks. In: 2016 4th international symposium on computational and business intelligence (ISCBI). IEEE, pp 101–107 – reference: EmaryEYamanyWHassanienAESnaselVMulti-objective gray-wolf optimization for attribute reductionProcedia Comput Sci20156562363210.1016/j.procs.2015.09.006 – reference: Mostafa A, Fouad A, Houseni M, Allam N, Hassanien AE, Hefny H, Aslanishvili I (2016) A hybrid grey wolf based segmentation with statistical image for ct liver images. In: International conference on advanced intelligent systems and informatics. Springer, pp 846–855 – reference: VermaSKYadavSNagarSKOptimization of fractional order pid controller using grey wolf optimizerJ Control Autom Electr Syst2017281910.1007/s40313-017-0305-3 – reference: EmaryEZawbaaHMGrosanCHassenianAEFeature subset selection approach by gray-wolf optimization2015ChamSpringer113 – reference: Chaman-MotlaghASuperdefect photonic crystal filter optimization using grey wolf optimizerIEEE Photonics Technol Lett201527222355235810.1109/LPT.2015.2464332 – reference: BäckTFogelDBMichalewiczZHandbook of evolutionary computation1997New YorkOxford University Press10.1887/07503089580883.68001 – reference: StornRPriceKDifferential evolution-a simple and efficient heuristic for global optimization over continuous spacesJ Global Optim1997114341359147955310.1023/A:10082028213280888.90135 – reference: RameshkumarJGanesanSAbiramiMSubramanianSCost, emission and reserve pondered pre-dispatch of thermal power generating units coordinated with real coded grey wolf optimisationIET Gener Trans Distrib201610497298510.1049/iet-gtd.2015.0726 – reference: AliMElhameedMAFarahatMAEffective parameters identification for polymer electrolyte membrane fuel cell models using grey wolf optimizerRenew Energy201711145546210.1016/j.renene.2017.04.036 – reference: SharmaSBhattacharjeeSBhattacharyaAGrey wolf optimisation for optimal sizing of battery energy storage device to minimise operation cost of microgridIET Gener Trans Distrib201610362563710.1049/iet-gtd.2015.0429 – reference: WangLYueJSuYLuFSunQA novel remaining useful life prediction approach for superbuck converter circuits based on modified grey wolf optimizer-support vector regressionEnergies201710445910.3390/en10040459 – reference: KumarVChhabraJKKumarDGrey wolf algorithm-based clustering techniqueJ Intell Syst20172611531683605261 – reference: LuCGaoLLiXXiaoSA hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industryEng Appl Artif Intell201757617910.1016/j.engappai.2016.10.013 – reference: Hansen N, Kern S (2004) Evaluating the CMA evolution strategy on multimodal test functions. In: International conference on parallel problem solving from nature. Springer, pp 282–291 – reference: GuptaESaxenaAGrey wolf optimizer based regulator design for automatic generation control of interconnected power systemCogent Eng2016311151612 – reference: Yang X-S (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, pp 169–178 – reference: KambojVKBathSKDhillonJSSolution of non-convex economic load dispatch problem using grey wolf optimizerNeural Comput Appl20152711610.1162/NECO_a_00684 – reference: MirjaliliSMirjaliliSMHatamlouAMulti-verse optimizer: a nature-inspired algorithm for global optimizationNeural Comput Appl201627249551310.1007/s00521-015-1870-7 – reference: ZhangSZhouYTemplate matching using grey wolf optimizer with lateral inhibitionOptik20171301229124310.1016/j.ijleo.2016.11.173 – reference: MirjaliliSSaremiSMirjaliliSMCoelhoLSMulti-objective grey wolf optimizer: a novel algorithm for multi-criterion optimizationExpert Syst Appl20164710611910.1016/j.eswa.2015.10.039 – reference: Sangwan V, Kumar R, Rathore AK (2016) Estimation of battery parameters of the equivalent circuit model using grey wolf optimization. In: 2016 IEEE 6th international conference on power systems (ICPS). IEEE, pp 1–6 – reference: SulaimanMHMustaffaZMohamedMRAlimanOUsing the gray wolf optimizer for solving optimal reactive power dispatch problemAppl Soft Comput20153228629210.1016/j.asoc.2015.03.041 – reference: SultanaUKhairuddin AzharBMokhtarASZareenNBeenishSGrey wolf optimizer based placement and sizing of multiple distributed generation in the distribution systemEnergy201611152553610.1016/j.energy.2016.05.128 – reference: MadadiAMotlaghMMOptimal control of dc motor using grey wolf optimizer algorithmTJEAS Journal-2014-4-04/373-37920144437379 – reference: MohantySSubudhiBRayPKA new mppt design using grey wolf optimization technique for photovoltaic system under partial shading conditionsIEEE Trans Sustain Energy20167118118810.1109/TSTE.2015.2482120 – reference: SaremiSMirjaliliSZMirjaliliSMEvolutionary population dynamics and grey wolf optimizerNeural Comput Appl20152651257126310.1007/s00521-014-1806-7 – reference: MirjaliliSMoth-flame optimization algorithm: a novel nature-inspired heuristic paradigmKnowl-Based Syst20158922824910.1016/j.knosys.2015.07.006 – reference: Muangkote N, Sunat K, Chiewchanwattana S (2014) An improved grey wolf optimizer for training q-gaussian radial basis functional-link nets. In: 2014 international computer science and engineering conference (ICSEC), pp 209–214 – reference: Mallick RK, Debnath MK, Haque F, Rout RR (2016) Application of grey wolves-based optimization technique in multi-area automatic generation control. In: International conference on electrical, electronics, and optimization techniques (ICEEOT) – reference: MedjahedSAAitSTBenyettouAOualiMGray wolf optimizer for hyperspectral band selectionAppl Soft Comput20164017818610.1016/j.asoc.2015.09.045 – reference: ČrepinšekMLiuS-HMernikMExploration and exploitation in evolutionary algorithms: a surveyACM Comput Surv (CSUR)2013453351293.68251 – reference: KarnavasYLChasiotisIDPeponakisELPermanent magnet synchronous motor design using grey wolf optimizer algorithmInt J Electr Comput Eng (IJECE)2016632016 – reference: EngelbrechtAPComputational intelligence: an introduction2007HobokenWiley10.1002/9780470512517 – reference: ZhangSZhouYGrey wolf optimizer based on Powell local optimization method for clustering analysisDiscret Dyn Nat Soc2015201517 – reference: YangBZhangXYuTShuHFangZGrouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbineEnergy Convers Manag201613342744310.1016/j.enconman.2016.10.062 – reference: Dao TK (2016) Enhanced diversity herds grey wolf optimizer for optimal area coverage in wireless sensor networks. In: Genetic and evolutionary computing: proceedings of the tenth international conference on genetic and evolutionary computing, November 7–9, 2016 Fuzhou City, Fujian Province, China, vol 536. Springer, p 174 – reference: PowellMJDRestart procedures for the conjugate gradient methodMath Program197712124125447862210.1007/BF015937900396.90072 – reference: Rodríguez L, Castillo O, Soria J (2017) A study of parameters of the grey wolf optimizer algorithm for dynamic adaptation with fuzzy logic. In: Nature-inspired design of hybrid intelligent systems. Springer, pp 371–390 – reference: LiSXWangJSDynamic modeling of steam condenser and design of pi controller based on grey wolf optimizerMath Probl Eng201520159 – reference: EngelbrechtAPFundamentals of computational swarm intelligence2006HobokenWiley – volume: 9 start-page: 523 issue: 4 year: 2015 ident: 3272_CR43 publication-title: Int J Energy Sect Manag doi: 10.1108/IJESM-09-2014-0003 – volume: 43 start-page: 150 issue: 1 year: 2015 ident: 3272_CR77 publication-title: Appl Intell doi: 10.1007/s10489-014-0645-7 – volume: 2015 start-page: 17 year: 2015 ident: 3272_CR139 publication-title: Discret Dyn Nat Soc – ident: 3272_CR51 doi: 10.1109/ICELMACH.2016.7732627 – ident: 3272_CR30 doi: 10.1109/VTCFall.2014.6966100 – volume: 133 start-page: 427 year: 2017 ident: 3272_CR135 publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.10.062 – start-page: 25 volume-title: A survey of clustering data mining techniques year: 2006 ident: 3272_CR4 – ident: 3272_CR47 doi: 10.1109/ICITEED.2015.7408911 – volume: 27 start-page: 495 issue: 2 year: 2016 ident: 3272_CR82 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1870-7 – ident: 3272_CR19 doi: 10.1109/SOCPAR.2015.7492781 – volume: 8 start-page: 109 year: 2015 ident: 3272_CR57 publication-title: J Comput Sci doi: 10.1016/j.jocs.2015.03.011 – volume: 26 start-page: 1257 issue: 5 year: 2015 ident: 3272_CR111 publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1806-7 – ident: 3272_CR39 – ident: 3272_CR15 doi: 10.1109/MHS.1995.494215 – ident: 3272_CR38 doi: 10.1109/ICEC.1997.592258 – volume: 33 start-page: 1327 year: 2016 ident: 3272_CR60 publication-title: Qual Reliab Eng Int doi: 10.1002/qre.2107 – ident: 3272_CR125 – volume: 65 start-page: 623 year: 2015 ident: 3272_CR20 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2015.09.006 – volume: 7 start-page: 181 issue: 1 year: 2016 ident: 3272_CR87 publication-title: IEEE Trans Sustain Energy doi: 10.1109/TSTE.2015.2482120 – volume: 5 start-page: 988 year: 1999 ident: 3272_CR127 publication-title: IEEE Trans Neural Netw doi: 10.1109/72.788640 – volume: 6 start-page: 2016 issue: 3 year: 2016 ident: 3272_CR52 publication-title: Int J Electr Comput Eng (IJECE) – volume: 57 start-page: 315 year: 2017 ident: 3272_CR107 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.03.048 – volume: 73 start-page: 853 year: 2015 ident: 3272_CR115 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2015.06.005 – volume: 75 start-page: 147 year: 2015 ident: 3272_CR119 publication-title: Soil Dyn Earthq Eng doi: 10.1016/j.soildyn.2015.04.004 – start-page: 1 volume-title: Feature subset selection approach by gray-wolf optimization year: 2015 ident: 3272_CR21 – volume: 89 start-page: 228 year: 2015 ident: 3272_CR78 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2015.07.006 – volume: 2016 start-page: 11 year: 2016 ident: 3272_CR112 publication-title: Int J Antennas Propag doi: 10.1155/2016/1205970 – ident: 3272_CR91 – volume: 8 start-page: 035903 issue: 3 year: 2016 ident: 3272_CR36 publication-title: J Renew Sustain Energy doi: 10.1063/1.4950945 – volume: 29 start-page: 31 issue: 3 year: 1996 ident: 3272_CR40 publication-title: Computer doi: 10.1109/2.485891 – volume: 10 start-page: 625 issue: 3 year: 2016 ident: 3272_CR114 publication-title: IET Gener Trans Distrib doi: 10.1049/iet-gtd.2015.0429 – volume: 3 start-page: 1256083 issue: 1 year: 2016 ident: 3272_CR14 publication-title: Cogent Eng doi: 10.1080/23311916.2016.1256083 – volume: 133 start-page: 149 year: 2016 ident: 3272_CR113 publication-title: Electr Power Syst Res doi: 10.1016/j.epsr.2015.12.019 – volume-title: Computational intelligence: an introduction year: 2007 ident: 3272_CR24 doi: 10.1002/9780470512517 – volume-title: The nature of statistical learning theory year: 1995 ident: 3272_CR126 doi: 10.1007/978-1-4757-2440-0 – ident: 3272_CR74 doi: 10.1109/ICEEOT.2016.7755160 – volume: 32 start-page: 340 year: 2016 ident: 3272_CR88 publication-title: IEEE Trans Energy Convers doi: 10.1109/TEC.2016.2633722 – volume-title: Genetic programming: on the programming of computers by means of natural selection year: 1992 ident: 3272_CR59 – volume: 111 start-page: 455 year: 2017 ident: 3272_CR2 publication-title: Renew Energy doi: 10.1016/j.renene.2017.04.036 – volume-title: Handbook of evolutionary computation year: 1997 ident: 3272_CR3 doi: 10.1887/0750308958 – volume: 2016 start-page: 8 year: 2016 ident: 3272_CR85 publication-title: Appl Comput Intell Soft Comput doi: 10.1155/2016/7950348 – volume: 69 start-page: 46 year: 2014 ident: 3272_CR83 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2013.12.007 – ident: 3272_CR35 doi: 10.1063/1.4973255 – ident: 3272_CR105 doi: 10.1109/CEC.2016.7744183 – volume: 26 start-page: 317 issue: 2 year: 2015 ident: 3272_CR143 publication-title: J Syst Eng Electron doi: 10.1109/JSEE.2015.00037 – ident: 3272_CR58 doi: 10.1088/1757-899X/83/1/012014 – volume: 40 start-page: 178 year: 2016 ident: 3272_CR75 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2015.09.045 – ident: 3272_CR133 doi: 10.1007/978-3-319-26690-9_22 – volume: 35 start-page: 1513 issue: 5 year: 2016 ident: 3272_CR25 publication-title: COMPEL-The Int J Comput Math Electr Electron Eng doi: 10.1108/COMPEL-09-2015-0337 – volume: 9 start-page: 4 issue: 1 year: 2015 ident: 3272_CR70 publication-title: Algorithms doi: 10.3390/a9010004 – volume: 5 start-page: 015 year: 2015 ident: 3272_CR136 publication-title: J Jiangxi Univ Sci Technol – ident: 3272_CR109 doi: 10.1109/ICPES.2016.7584086 – ident: 3272_CR50 – volume: 11 start-page: 183 issue: 2 year: 2015 ident: 3272_CR42 publication-title: Int J Energy Technol Policy doi: 10.1504/IJETP.2015.069821 – volume: 83 start-page: 80 year: 2015 ident: 3272_CR76 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2015.01.010 – ident: 3272_CR5 doi: 10.1007/978-981-10-0135-2_13 – volume: 4 start-page: 6438 year: 2016 ident: 3272_CR64 publication-title: IEEE Access doi: 10.1109/ACCESS.2016.2613940 – volume: 10 start-page: 459 issue: 4 year: 2017 ident: 3272_CR130 publication-title: Energies doi: 10.3390/en10040459 – volume: 10 start-page: 53 issue: 3 year: 2016 ident: 3272_CR104 publication-title: Majlesi J Electr Eng – ident: 3272_CR9 – volume: 27 start-page: 2355 issue: 22 year: 2015 ident: 3272_CR6 publication-title: IEEE Photonics Technol Lett doi: 10.1109/LPT.2015.2464332 – year: 2017 ident: 3272_CR54 publication-title: Pract Exp Concurr Comput doi: 10.1002/cpe.4044 – volume: 2015 start-page: 9 year: 2015 ident: 3272_CR66 publication-title: Math Probl Eng – volume: 27 start-page: 2031 issue: 7 year: 2016 ident: 3272_CR96 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1996-7 – volume: 99 start-page: 121 year: 2016 ident: 3272_CR141 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2016.05.015 – ident: 3272_CR97 – volume: 27 start-page: 1 year: 2015 ident: 3272_CR49 publication-title: Neural Comput Appl doi: 10.1162/NECO_a_00684 – ident: 3272_CR137 doi: 10.1007/978-3-642-04944-6_14 – volume: 3 start-page: 1 issue: 1 year: 2015 ident: 3272_CR17 publication-title: Univ J Commun Netw doi: 10.13189/ujcn.2015.030101 – ident: 3272_CR28 doi: 10.1109/ICENCO.2015.7416358 – volume: 1 start-page: 53 issue: 1 year: 1997 ident: 3272_CR13 publication-title: IEEE Trans Evol Comput doi: 10.1109/4235.585892 – volume: 12 start-page: 241 issue: 1 year: 1977 ident: 3272_CR98 publication-title: Math Program doi: 10.1007/BF01593790 – volume: 130 start-page: 1229 year: 2017 ident: 3272_CR140 publication-title: Optik doi: 10.1016/j.ijleo.2016.11.173 – volume: 133 start-page: 427 year: 2016 ident: 3272_CR134 publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2016.10.062 – ident: 3272_CR86 doi: 10.1109/IJCNN.2015.7280704 – volume: 7 start-page: 671 issue: 3 year: 2017 ident: 3272_CR12 publication-title: Asian J Res Soc Sci Hum – volume: 43 start-page: 1548 issue: 13 year: 2015 ident: 3272_CR16 publication-title: Electric Power Compon Syst doi: 10.1080/15325008.2015.1041625 – volume: 7 start-page: 838 issue: 5 year: 2014 ident: 3272_CR118 publication-title: Int Rev Model Simul (IREMOS) doi: 10.15866/iremos.v7i5.2799 – volume: 27 start-page: 1643 issue: 6 year: 2016 ident: 3272_CR48 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1962-4 – volume: 28 start-page: 1 year: 2017 ident: 3272_CR128 publication-title: J Control Autom Electr Syst doi: 10.1007/s40313-017-0305-3 – volume: 4 start-page: 373 issue: 4 year: 2014 ident: 3272_CR71 publication-title: TJEAS Journal-2014-4-04/373-379 – volume: 11 start-page: 341 issue: 4 year: 1997 ident: 3272_CR120 publication-title: J Global Optim doi: 10.1023/A:1008202821328 – ident: 3272_CR138 – volume: 74 start-page: 252 year: 2016 ident: 3272_CR44 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2015.07.031 – ident: 3272_CR106 doi: 10.1007/978-3-319-47054-2_25 – ident: 3272_CR93 doi: 10.1109/ICSECS.2015.7333107 – volume: 32 start-page: 286 year: 2015 ident: 3272_CR122 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2015.03.041 – ident: 3272_CR7 doi: 10.1109/ICACCI.2016.7732343 – ident: 3272_CR33 doi: 10.1109/ICRITO.2015.7359368 – ident: 3272_CR45 doi: 10.1109/ICACCI.2015.7275611 – volume: 49 start-page: 55 issue: 5 year: 2016 ident: 3272_CR101 publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2016.07.089 – volume: 2017 start-page: 16 year: 2017 ident: 3272_CR63 publication-title: Comput Intell Neurosci – volume: 45 start-page: 1 year: 2017 ident: 3272_CR27 publication-title: Electric Power Compon Syst doi: 10.1080/15325008.2017.1292567 – ident: 3272_CR89 – volume: 27 start-page: 1053 issue: 4 year: 2016 ident: 3272_CR79 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1920-1 – ident: 3272_CR18 doi: 10.1007/978-3-319-26690-9_27 – volume: 86 start-page: 64 year: 2017 ident: 3272_CR53 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2017.04.029 – volume: 28 start-page: S421 issue: Suppl 1 year: 2016 ident: 3272_CR67 publication-title: Neural Comput Appl – volume: 13 start-page: 54 issue: 1 year: 2015 ident: 3272_CR102 publication-title: Aust J Electr Electron Eng doi: 10.1080/1448837X.2015.1092933 – volume: 5 start-page: 14807 year: 2017 ident: 3272_CR110 publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2726586 – volume: 10 start-page: 2108 issue: 9 year: 2016 ident: 3272_CR142 publication-title: IET Gener Transm Distrib doi: 10.1049/iet-gtd.2015.1141 – ident: 3272_CR124 doi: 10.1007/978-3-319-26690-9_21 – volume: 49 start-page: 778 issue: 1 year: 2016 ident: 3272_CR132 publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2016.03.151 – volume: 26 start-page: 153 issue: 1 year: 2017 ident: 3272_CR61 publication-title: J Intell Syst doi: 10.1515/jisys-2014-0137 – year: 2016 ident: 3272_CR55 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2644-6 – ident: 3272_CR11 – ident: 3272_CR26 doi: 10.5220/0006048201710177 – ident: 3272_CR56 doi: 10.1007/978-981-10-0448-3_87 – ident: 3272_CR73 doi: 10.1109/ICCIC.2015.7435714 – ident: 3272_CR92 doi: 10.1109/ICSEC.2014.6978196 – year: 2017 ident: 3272_CR121 publication-title: Multimed Tools Appl doi: 10.1007/s11042-016-4312-3 – volume: 64 start-page: 527 issue: 1 year: 2017 ident: 3272_CR100 publication-title: IEEE Trans Industr Electron doi: 10.1109/TIE.2016.2607698 – volume: 15 start-page: 328 issue: 4 year: 2016 ident: 3272_CR46 publication-title: Int J Data Min Bioinf doi: 10.1504/IJDMB.2016.078151 – volume: 172 start-page: 371 year: 2016 ident: 3272_CR22 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.06.083 – volume: 47 start-page: 106 year: 2016 ident: 3272_CR84 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2015.10.039 – volume: 111 start-page: 630 year: 2016 ident: 3272_CR41 publication-title: Energy doi: 10.1016/j.energy.2016.05.105 – ident: 3272_CR94 doi: 10.1109/ICIEV.2015.7334054 – volume-title: Fundamentals of computational swarm intelligence year: 2006 ident: 3272_CR23 – ident: 3272_CR10 doi: 10.1109/ICSCTI.2015.7489575 – ident: 3272_CR1 doi: 10.1109/ISCBI.2016.7743266 – volume: 99 start-page: 161 year: 2016 ident: 3272_CR69 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2016.06.004 – volume: 96 start-page: 120 year: 2016 ident: 3272_CR80 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2015.12.022 – volume: 12 start-page: 702 issue: 6 year: 2008 ident: 3272_CR116 publication-title: IEEE Trans Evol Comput doi: 10.1109/TEVC.2008.919004 – volume: 3 start-page: 1151612 issue: 1 year: 2016 ident: 3272_CR32 publication-title: Cogent Eng doi: 10.1080/23311916.2016.1151612 – volume: 98 start-page: 411 year: 2015 ident: 3272_CR72 publication-title: Energy Convers Manag doi: 10.1016/j.enconman.2015.04.005 – volume: 111 start-page: 525 year: 2016 ident: 3272_CR123 publication-title: Energy doi: 10.1016/j.energy.2016.05.128 – volume: 10 start-page: 972 issue: 4 year: 2016 ident: 3272_CR103 publication-title: IET Gener Trans Distrib doi: 10.1049/iet-gtd.2015.0726 – volume: 114 start-page: 55 year: 2016 ident: 3272_CR117 publication-title: J Supercrit Fluids doi: 10.1016/j.supflu.2016.04.006 – ident: 3272_CR131 doi: 10.1109/PECON.2014.7062431 – volume: 45 start-page: 35 issue: 3 year: 2013 ident: 3272_CR8 publication-title: ACM Comput Surv (CSUR) doi: 10.1145/2480741.2480752 – volume: 92 start-page: 99 year: 2016 ident: 3272_CR62 publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2016.07.329 – ident: 3272_CR37 doi: 10.1007/978-3-540-30217-9_29 – volume: 134 start-page: 168 year: 2016 ident: 3272_CR95 publication-title: Atmos Environ doi: 10.1016/j.atmosenv.2016.03.056 – ident: 3272_CR129 doi: 10.1109/ICCKE.2015.7365818 – volume: 83 start-page: 325 year: 2016 ident: 3272_CR99 publication-title: Int J Electr Power Energy Syst doi: 10.1016/j.ijepes.2016.04.034 – volume: 26 start-page: 393 issue: 4 year: 2016 ident: 3272_CR90 publication-title: Neural Netw World doi: 10.14311/NNW.2016.26.023 – volume: 2017 start-page: 15 year: 2017 ident: 3272_CR65 publication-title: Comput Math Methods Med doi: 10.1155/2017/9512741 – volume: 95 start-page: 51 year: 2016 ident: 3272_CR81 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2016.01.008 – volume: 52 start-page: 64 year: 2017 ident: 3272_CR108 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2016.12.022 – volume: 5 start-page: 511 issue: 4 year: 2015 ident: 3272_CR31 publication-title: J Optim Civ Eng – volume: 17 start-page: 4831 issue: 12 year: 2012 ident: 3272_CR29 publication-title: Commun Nonlinear Sci Numer Simul doi: 10.1016/j.cnsns.2012.05.010 – ident: 3272_CR34 – volume: 57 start-page: 61 year: 2017 ident: 3272_CR68 publication-title: Eng Appl Artif Intell doi: 10.1016/j.engappai.2016.10.013 |
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| Snippet | Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems... |
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| Title | Grey wolf optimizer: a review of recent variants and applications |
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