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Recent advances in multi-objective grey wolf optimizer, its versions and applications

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Abstract

In this work, a comprehensive review of the multi-objective grey wolf optimizer (MOGWO) is provided. In multi-objective optimization (MO), more than one objective function must be considered at the same time. To deal with such problems, a priori or a posteriori MOGWO variants have been proposed in the literature. In the a priori model, the multi-objective functions are aggregated into a single objective function by a number of weights. In the posterior model, the multi-objective formulation is maintained and MOGWO is employed to estimate the Pareto optimal solutions representing the best trade-offs between the objectives. Due to the successful performance of MOGWO, it has been widely utilized for MO. This review covers the research growth of MOGWO in terms of a number of researches, topics, top researchers, etc. Furthermore, several versions of MOGWO have been introduced and reviewed with applications in diverse fields. This work also provides a critical analysis to show the shortcomings and limitations of using the basic version of MOGWO followed by several future directions. This review paper will be a base paper for any researcher interested to implement MOGWO in its work.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Deb K (2014) Multi-objective optimization. In: Burke E, Kendall G (eds) Search methodologies. Springer, Boston, pp 403–449

    Chapter  Google Scholar 

  2. Collette Y, Siarry P (2004) Multiobjective optimization: principles and case studies. Springer, Berlin

    Book  MATH  Google Scholar 

  3. Branke J, Kaußler T, Schmeck H (2001) Guidance in evolutionary multi-objective optimization. Adv Eng Softw 32(6):499–507

    Article  MATH  Google Scholar 

  4. Marler RT, Arora JS (2004) Survey of multi-objective optimization methods for engineering. Struct Multidiscip Optim 26(6):369–395

    Article  MathSciNet  MATH  Google Scholar 

  5. Mirjalili S, Gandomi AH, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM (2017) Salp swarm algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191

    Article  Google Scholar 

  6. Deb K (2012) Advances in evolutionary multi-objective optimization. In: International symposium on search based software engineering. Springer, pp 1–26

  7. Mirjalili S, Saremi S, Mirjalili SM, Coelho L.d.S (2016) Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization. Expert Syst Appl 47:106–119

    Article  Google Scholar 

  8. Branke J, Deb K (2005) Integrating user preferences into evolutionary multi-objective optimization. In: Jin Y (ed) Knowledge incorporation in evolutionary computation. Springer, Berlin, pp 461–477

    Chapter  Google Scholar 

  9. Coello CAC, Lamont GB, Van Veldhuizen DA et al (2007) Evolutionary algorithms for solving multi-objective problems, vol 5. Springer, Berlin

    MATH  Google Scholar 

  10. Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the first international conference on genetic algorithms and their applications, 1985. Lawrence Erlbaum Associates. Inc

  11. Sun X, Yoon JY (2018) Multi-objective optimization of a gas cyclone separator using genetic algorithm and computational fluid dynamics. Powder Technol 325:347–360

    Article  Google Scholar 

  12. Liu C, Liu L (2017) Optimal design of Li-ion batteries through multi-physics modeling and multi-objective optimization. J Electrochem Soc 164(11):3254

    Article  Google Scholar 

  13. Starke AR, Cardemil JM, Escobar R, Colle S (2018) Multi-objective optimization of hybrid CSP + PV system using genetic algorithm. Energy 147:490–503

    Article  Google Scholar 

  14. Li A-D, Xue B, Zhang M (2020) Multi-objective feature selection using hybridization of a genetic algorithm and direct multisearch for key quality characteristic selection. Inf Sci 523:245–265

    Article  MathSciNet  Google Scholar 

  15. Tanabe R, Ishibuchi H (2019) A review of evolutionary multimodal multiobjective optimization. IEEE Trans Evol Comput 24(1):193–200

    Article  Google Scholar 

  16. Sengupta S, Das S, Nasir M, Vasilakos AV, Pedrycz W (2012) An evolutionary multiobjective sleep-scheduling scheme for differentiated coverage in wireless sensor networks. IEEE Trans Syst Man Cybern C (Appl Rev) 42(6):1093–1102

    Article  Google Scholar 

  17. Wu M, Xiong N, Vasilakos AV, Leung VC, Chen CP (2020) RNN-K: a reinforced newton method for consensus-based distributed optimization and control over multiagent systems. IEEE Trans Cybern 52:4012–4026

  18. Yi J-H, Xing L-N, Wang G-G, Dong J, Vasilakos AV, Alavi AH, Wang L (2020) Behavior of crossover operators in NSGA-III for large-scale optimization problems. Inf Sci 509:470–487

    Article  MathSciNet  Google Scholar 

  19. Shehab M, Khader AT, Al-Betar MA (2017) A survey on applications and variants of the cuckoo search algorithm. Appl Soft Comput 61:1041–1059

    Article  Google Scholar 

  20. Zhang Z, Qin H, Yao L, Liu Y, Jiang Z, Feng Z, Ouyang S (2020) Improved multi-objective moth-flame optimization algorithm based on R-domination for cascade reservoirs operation. J Hydrol 581:124431

    Article  Google Scholar 

  21. Mirjalili S, Jangir P, Saremi S (2017) Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems. Appl Intell 46(1):79–95

    Article  Google Scholar 

  22. Abd Elaziz M, Oliva D, Ewees AA, Xiong S (2019) Multi-level thresholding-based grey scale image segmentation using multi-objective multi-verse optimizer. Expert Syst Appl 125:112–129

    Article  Google Scholar 

  23. Yüzgeç U, Kusoglu M (2020) Multi-objective Harris Hawks optimizer for multiobjective optimization problems. BSEU J Eng Res Technol 1(1):31–41

    Google Scholar 

  24. Makhadmeh SN, Khader AT, Al-Betar MA, Naim S (2019) Multi-objective power scheduling problem in smart homes using grey wolf optimiser. J Ambient Intell Humaniz Comput 10(9):3643–3667

    Article  Google Scholar 

  25. Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30(2):413–435

    Article  Google Scholar 

  26. El-Fergany AA, Hasanien HM (2015) Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms. Electr Power Compon Syst 43(13):1548–1559

    Article  Google Scholar 

  27. Nuaekaew K, Artrit P, Pholdee N, Bureerat S (2017) Optimal reactive power dispatch problem using a two-archive multi-objective grey wolf optimizer. Expert Syst Appl 87:79–89

    Article  Google Scholar 

  28. Li C, Wang W, Chen D (2019) Multi-objective complementary scheduling of hydro-thermal-re power system via a multi-objective hybrid grey wolf optimizer. Energy 171:241–255

    Article  Google Scholar 

  29. Lu C, Gao L, Li X, Xiao S (2017) A hybrid multi-objective grey wolf optimizer for dynamic scheduling in a real-world welding industry. Eng Appl Artif Intell 57:61–79

    Article  Google Scholar 

  30. Lu C, Xiao S, Li X, Gao L (2016) An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production. Adv Eng Softw 99:161–176

    Article  Google Scholar 

  31. Lu C, Gao L, Pan Q, Li X, Zheng J (2019) A multi-objective cellular grey wolf optimizer for hybrid flowshop scheduling problem considering noise pollution. Appl Soft Comput J 75:728–749

    Article  Google Scholar 

  32. Nguyen T, Thom HTH, Dao T (2017) Estimation localization in wireless sensor network based on multi-objective grey wolf optimizer. Adv Intell Syst Comput AISC 538:228–237

    Google Scholar 

  33. Pingale RP, Shinde SN (2021) Multi-objective sunflower based grey wolf optimization algorithm for multipath routing in IoT network. Wirel Pers Commun 117(3):1909–1930

    Article  Google Scholar 

  34. Fatima A, Javaid N, Butt AA, Sultana T, Hussain W, Bilal M, Hashmi MAR, Akbar M, Ilahi M (2019) An enhanced multi-objective gray wolf optimization for virtual machine placement in cloud data centers. Electronics (Switzerland) 8(2):218

    Google Scholar 

  35. Sreenu K, Malempati S (2019) MFGMTS: epsilon constraint-based modified fractional grey wolf optimizer for multi-objective task scheduling in cloud computing. IETE J Res 65(2):201–215

    Article  Google Scholar 

  36. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  37. Behnood A, Golafshani EM (2018) Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves. J Clean Prod 202:54–64

    Article  Google Scholar 

  38. Emary E, Yamany W, Hassanien AE, Snasel V (2015) Multi-objective gray-wolf optimization for attribute reduction. Procedia Comput Sci 65:623–632

    Article  Google Scholar 

  39. Luo S, Zhang L, Fan Y (2019) Energy-efficient scheduling for multi-objective flexible job shops with variable processing speeds by grey wolf optimization. J Clean Prod 234:1365–1384

    Article  Google Scholar 

  40. Qin H, Fan P, Tang H, Huang P, Fang B, Pan S (2019) An effective hybrid discrete grey wolf optimizer for the casting production scheduling problem with multi-objective and multi-constraint. Comput Ind Eng 128:458–476

    Article  Google Scholar 

  41. Zapotecas-Martínez S, García-Nájera A, López-Jaimes A (2019) Multi-objective grey wolf optimizer based on decomposition. Expert Syst Appl 120:357–371

    Article  Google Scholar 

  42. Kumar A, Pant S, Ram M, Chaube S (2019) Multi-objective grey wolf optimizer approach to the reliability-cost optimization of life support system in space capsule. Int J Syst Assur Eng Manag 10:276–284

    Article  Google Scholar 

  43. Moazzami M, Ghanbari M, Shahinzadeh H, Moradi J, Gharehpetian GB (2018) Application of multi-objective grey wolf algorithm on energy management of microgrids with techno-economic and environmental considerations. In: 3rd conference on swarm intelligence and evolutionary computation, CSIEC 2018, pp 1–9

  44. Lu H, Ma X, Huang K, Azimi M (2020) Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer. Renew Sustain Energy Rev 127:109856

    Article  Google Scholar 

  45. Yousri D, Thanikanti SB, Balasubramanian K, Osama A, Fathy A (2020) Multi-objective grey wolf optimizer for optimal design of switching matrix for shaded PV array dynamic reconfiguration. IEEE Access 8:159931–159946

    Article  Google Scholar 

  46. Makhadmeh SN, Khader AT, Al-Betar MA, Naim S (2018) Multi-objective power scheduling problem in smart homes using grey wolf optimiser. J Ambient Intell Humaniz Comput 10:3643–3667

    Article  Google Scholar 

  47. Dilip L, Bhesdadiya R, Trivedi I, Jangir P (2018) Optimal power flow problem solution using multi-objective grey wolf optimizer algorithm. Lect Notes Netw Syst 19:191–201

    Article  Google Scholar 

  48. Alsadie D (2021) TSMGWO: optimizing task schedule using multi-objectives grey wolf optimizer for cloud data centers. IEEE Access 9:37707–37725

    Article  Google Scholar 

  49. Sukpancharoen S, Srinophakun TR, Aungkulanon P (2020) Grey wolf optimizer (GWO) with multi-objective optimization for biodiesel production from waste cooking oil using central composite design (CCD). Int J Mech Eng Robot Res 9(8):1219–1225

    Article  Google Scholar 

  50. Makhadmeh SN, Al-Betar MA, Alyasseri ZAA, Abasi AK, Khader AT, Damaševičius R, Mohammed MA, Abdulkareem KH (2021) Smart home battery for the multi-objective power scheduling problem in a smart home using grey wolf optimizer. Electronics (Switzerland) 10(4):1–35

    Google Scholar 

  51. Selvaraj G, Rajangam K (2019) Multi-objective grey wolf optimizer algorithm for combination of network reconfiguration and D-STATCOM allocation in distribution system. Int Trans Electr Energy Syst 29(11):e12100

    Article  Google Scholar 

  52. Tlili S, Mnasri S, Val T (2021) A multi-objective gray wolf algorithm for routing in IoT collection networks with real experiments. In: Proceedings—2021 IEEE 4th national computing colleges conference, NCCC 2021

  53. Ojha A, Chanak P (2021) Multi-objective gray wolf optimization based data routing scheme for wireless sensor networks. IEEE Internet Things J 9:4615–4623

    Article  Google Scholar 

  54. Sultana B, Mustafa MW (2015) Grey wolf optimization algorithm for multi-objective distribution system reconfiguration considering greenhouse gas emissions and loss. Eng Intell Syst 23(4):197–208

    Google Scholar 

  55. Alamiedy TA, Anbar M, Alqattan ZNM, Alzubi QM (2020) Anomaly-based intrusion detection system using multi-objective grey wolf optimisation algorithm. J Ambient Intell Humaniz Comput 11(9):3735–3756

    Article  Google Scholar 

  56. Yildirim G, Alatas B (2021) New adaptive intelligent grey wolf optimizer based multi-objective quantitative classification rules mining approaches. J Ambient Intell Humaniz Comput 12(10):9611–9635

    Article  Google Scholar 

  57. Kamalova A, Navruzov S, Qian D, Lee SG (2019) Multi-robot exploration based on multi-objective grey wolf optimizer. Appl Sci (Switzerland) 9(14):2931

    Google Scholar 

  58. Kahla S, Soufi Y, Sedraoui M, Bechouat M (2017) Maximum power point tracking of wind energy conversion system using multi-objective grey wolf optimization of fuzzy-sliding mode controller. Int J Renew Energy Res 7(2):926–936

    Google Scholar 

  59. Chen J, Huang S, Shahabi L (2021) Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm. Appl Energy 298:117257

    Article  Google Scholar 

  60. Pandya SB, Jariwala HR (2020) Renewable energy resources integrated multi-objective optimal power flow using non-dominated sort grey wolf optimizer. J Green Eng 10(1):180–205

    Google Scholar 

  61. Habibollahzade A, Fakhari I, Mohsenian S, Aberoumand H, Taylor RA (2021) Multi-objective grey wolf optimization of solar chimneys based on an improved model incorporating a wind turbine power curve. Energy Convers Manag 239:114231

    Article  Google Scholar 

  62. Musharavati F, Khoshnevisan A, Alirahmi SM, Ahmadi P, Khanmohammadi S (2022) Multi-objective optimization of a biomass gasification to generate electricity and desalinated water using grey wolf optimizer and artificial neural network. Chemosphere 287:131980

    Article  Google Scholar 

  63. Al-Masri HMK, Al-Sharqi AA (2020) Technical design and optimal energy management of a hybrid photovoltaic biogas energy system using multi-objective grey wolf optimisation. IET Renew Power Gener 14(14):2765–2778

    Article  Google Scholar 

  64. Xuan H, Lin L, Qiao L, Zhou Y (2019) Grey wolf algorithm and multi-objective model for the manycast RSA problem in EONs. Information (Switzerland) 10(12):398

    Google Scholar 

  65. Luo Y, Lu C, Li X, Wang L, Gao L (2019) Green job shop scheduling problem with machine at different speeds using a multi-objective grey wolf optimization algorithm. In: IEEE international conference on automation science and engineering, vol 2019-August, pp 573–578

  66. Naserbegi A, Aghaie M, Zolfaghari A (2020) Implementation of grey wolf optimization (GWO) algorithm to multi-objective loading pattern optimization of a PWR reactor. Ann Nucl Energy 148:107703

    Article  Google Scholar 

  67. Gu X, Yang S, Liu Y, Hao R, Liu Z (2020) Multi-objective informative frequency band selection based on negentropy-induced grey wolf optimizer for fault diagnosis of rolling element bearings. Sensors (Switzerland) 20(7):1845

    Article  Google Scholar 

  68. Merikhi B, Mirjalili SM, Zoghi M, Mirjalili SZ, Mirjalili S (2019) Radiation pattern design of photonic crystal led optimized by using multi-objective grey wolf optimizer. Photon Netw Commun 38(1):167–176

    Article  Google Scholar 

  69. Meibody MNP, Naseh H, Ommi F (2019) A kriging based multi objective gray wolf optimization for hydrazine catalyst bed. Eng Solid Mech 7(3):179–192

    Article  Google Scholar 

  70. Dash TK, Solanki SS, Panda G, Satapathy SC (2021) Development of statistical estimators for speech enhancement using multi-objective grey wolf optimizer. Evol Intell 14(2):767–778

    Article  Google Scholar 

  71. Jiang X, Li J, Lu Y, Tian G (2020) Design of reverse logistics network for remanufacturing waste machine tools based on multi-objective gray wolf optimization algorithm. IEEE Access 8:141046–141056

    Article  Google Scholar 

  72. Al-Tashi Q, Abdulkadir SJ, Rais HM, Mirjalili S, Alhussian H, Ragab MG, Alqushaibi A (2020) Binary multi-objective grey wolf optimizer for feature selection in classification. IEEE Access 8:106247–106263

    Article  Google Scholar 

  73. Moldovan D, Slowik A (2021) Energy consumption prediction of appliances using machine learning and multi-objective binary grey wolf optimization for feature selection. Appl Soft Comput 111:107745

    Article  Google Scholar 

  74. Sahoo A, Chandra S (2017) Multi-objective grey wolf optimizer for improved cervix lesion classification. App Soft Comput J 52:64–80

    Article  Google Scholar 

  75. Gu J, Jiang T, Zhu H (2021) Energy-saving job shop scheduling problem with multi-objective discrete grey wolf optimization algorithm. Jisuanji Jicheng Zhizao Xitong/Comput Integr Manuf Syst CIMS 27(8):2295–2306

    Google Scholar 

  76. Karakoyun M, Gulcu S, Kodaz H (2021) D-MOSG: discrete multi-objective shuffled gray wolf optimizer for multi-level image thresholding. Eng Sci Technol Int J 24:1455–1466

    Google Scholar 

  77. Zhang Z, Guo X, Zhou M, Liu S, Qi L (2020) Multi-objective discrete grey wolf optimizer for solving stochastic multi-objective disassembly sequencing and line balancing problem. IEEE Trans Syst Man Cybern Syst 682–687

  78. Sreenu K, Malempati S (2018) FGMTS: fractional grey wolf optimizer for multi-objective task scheduling strategy in cloud computing. J Intell Fuzzy Syst 35(1):831–844

    Article  Google Scholar 

  79. Suresh M, Shatheesh Sam I (2020) Optimized interesting region identification for video steganography using fractional grey wolf optimization along with multi-objective cost function. J King Saud Univ Comput Inf Sci 34:3489–3496

  80. Fang W, Sun J, Chen H, Wu X (2016) A decentralized quantum-inspired particle swarm optimization algorithm with cellular structured population. Inf Sci 330:19–48

    Article  Google Scholar 

  81. Shi Y, Liu H, Gao L, Zhang G (2011) Cellular particle swarm optimization. Inf Sci 181(20):4460–4493

    Article  MathSciNet  MATH  Google Scholar 

  82. Biswas K, Vasant PM, Gamez Vintaned JA, Watada J (2021) Cellular automata-based multi-objective hybrid grey wolf optimization and particle swarm optimization algorithm for wellbore trajectory optimization. J Nat Gas Sci Eng 85:103695

    Article  Google Scholar 

  83. Jiang K, Ni H, Han R, Wang X (2019) An improved multi-objective grey wolf optimizer for dependent task scheduling in edge computing. Int J Innov Comput Inf Control 15(6):2289–2304

    Google Scholar 

  84. Zhao Y, Li W, Liu A (2020) Optimization of geometry quality model for wire and arc additive manufacture based on adaptive multi-objective grey wolf algorithm. Soft Comput 24(22):17401–17416

    Article  Google Scholar 

  85. Yang Z, Liu C, Qian W (2017) An improved multi-objective grey wolf optimization algorithm for fuzzy blocking flow shop scheduling problem. In: Proceedings of 2017 IEEE 2nd advanced information technology, electronic and automation control conference, IAEAC 2017, pp 661–667

  86. Khubroo M, Mousavirad SJ (2019) A levy flight-based decomposition multi-objective optimization based on grey wolf optimizer. In: 2019 9th international conference on computer and knowledge engineering, ICCKE 2019, pp 155–161

  87. Mohamed A-A, El-Gaafary AAM, Mohamed YS, Hemeida AM (2017) Multi-objective modified grey wolf optimizer for optimal power flow. In: 2016 18th international middle-east power systems conference, MEPCON 2016 - Proceedings, pp 982–990

  88. Korashy A, Kamel S, Nasrat L, Jurado F (2020) Developed multi-objective grey wolf optimizer with fuzzy logic decision-making tool for direction overcurrent relays coordination. Soft Comput 24(17):13305–13317

    Article  Google Scholar 

  89. Zhou B, Liu J, Yi L (2019) Multi-user power optimizationbased on multi-objective grey wolf optimizer. In: 2019 22nd international conference on electrical machines and systems, ICEMS 2019

  90. Yang Y, Yang B, Wang S, Jin T, Li S (2020) An enhanced multi-objective grey wolf optimizer for service composition in cloud manufacturing. Appl Soft Comput J 87:106003

    Article  Google Scholar 

  91. Liu J, Yang Z, Li D (2020) A multiple search strategies based grey wolf optimizer for solving multi-objective optimization problems. Expert Syst Appl 145:114134

    Article  Google Scholar 

  92. Javidsharifi M, Niknam T, Aghaei J, Mokryani G, Papadopoulos P (2019) Multi-objective day-ahead scheduling of microgrids using modified grey wolf optimizer algorithm. J Intell Fuzzy Syst 36(3):2857–2870

    Article  Google Scholar 

  93. Eappen G, Shankar T (2021) Multi-objective modified grey wolf optimization algorithm for efficient spectrum sensing in the cognitive radio network. Arab J Sci Eng 46(4):3115–3145

    Article  Google Scholar 

  94. Wang C, Zhang S, Xiao L, Fu T (2021) Wind speed forecasting based on multi-objective grey wolf optimisation algorithm, weighted information criterion, and wind energy conversion system: a case study in Eastern China. Energy Convers Manag 243:114402

    Article  Google Scholar 

  95. Meng K, Tang Q, Zhang Z, Yu C (2021) Solving multi-objective model of assembly line balancing considering preventive maintenance scenarios using heuristic and grey wolf optimizer algorithm. Eng Appl Artif Intell 100:104183

    Article  Google Scholar 

  96. Darvish Falehi A (2020) Novel harmonic elimination strategy based on multi-objective grey wolf optimizer to ameliorate voltage quality of odd-nary multi-level structure. Heliyon 6(3):e03585

    Article  Google Scholar 

  97. Kumar GVA, Narasimhamu KL (2020) Multi-objective optimization in WEDM of Inconel 750 alloy: application of TOPSIS embedded grey wolf optimizer. Adv Intell Syst Comput 949:231–240

    Google Scholar 

  98. Siddavaatam P, Sedaghat R (2019) Grey wolf optimizer driven design space exploration: a novel framework for multi-objective trade-off in architectural synthesis. Swarm Evol Comput 49:44–61

    Article  Google Scholar 

  99. Hashemi M, Javaheri D, Sabbagh P, Arandian B, Abnoosian K (2021) A multi-objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm. IET Commun 15:2342–2353

    Article  Google Scholar 

  100. Meng K, Tang Q, Zhang Z, Lu C, Deng M (2020) Integrated optimization of assembly line balance and preventive maintenance based on improved multi-objective grey wolf algorithm. Jisuanji Jicheng Zhizao Xitong/Comput Integr Manuf Syst CIMS 26(12):3302–3312

    Google Scholar 

  101. Xing H, Wu H, Chen Y, Zhang X (2020) Multi-efficiency optimization method of jamming resource based on multi-objective grey wolf optimizer. Beijing Hangkong Hangtian Daxue Xuebao/J Beijing Univ Aeronaut Astronaut 46(10):1990–1998

    Google Scholar 

  102. Wei GU (2020) An improved multi-objective grey wolf optimization algorithm with dynamic chaos local search mechanism. IEEE Joint Int Inf Technol Artif Intell Conf 9:2020–2024

  103. Kumar D, Kumar S, Bansal R (2020) Multi-objective multi-join query optimisation using modified grey wolf optimisation. Int J Adv Intell Parad 17(1–2):67–79

    Google Scholar 

  104. Makhadmeh SN, Khader AT, Al-Betar MA, Naim S, Abasi AK, Alyasseri ZAA (2021) A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home. Swarm Evol Comput 60:100793

    Article  Google Scholar 

  105. Yang Z, Liu C (2018) A hybrid multi-objective gray wolf optimization algorithm for a fuzzy blocking flow shop scheduling problem. Adv Mech Eng 10(3):1–13

  106. Zhu Z, Zhou X (2020) An efficient evolutionary grey wolf optimizer for multi-objective flexible job shop scheduling problem with hierarchical job precedence constraints. Comput Ind Eng 140:106280

    Article  Google Scholar 

  107. Gobalakrishnan N, Arun C (2018) A new multi-objective optimal programming model for task scheduling using genetic gray wolf optimization in cloud computing. Comput J 61(10):1523–1536

    Article  Google Scholar 

  108. Zhu W, Guo J, Zhao G, Zeng B (2020) Optimal sizing of an island hybrid microgrid based on improved multi-objective grey wolf optimizer. Processes 8(12):1–24

    Article  Google Scholar 

  109. Gokuldhev M, Singaravel G, Ram Mohan NR (2020) Multi-objective local pollination-based gray wolf optimizer for task scheduling heterogeneous cloud environment. J Circuits Syst Comput 29(7):2050100

    Article  Google Scholar 

  110. Karakoyun M, Ozkis A, Kodaz H (2020) A new algorithm based on gray wolf optimizer and shuffled frog leaping algorithm to solve the multi-objective optimization problems. Appl Soft Comput J 96:106560

    Article  Google Scholar 

  111. Karasu S, Saraç Z (2020) Classification of power quality disturbances by 2D-Riesz transform, multi-objective grey wolf optimizer and machine learning methods. Digit Signal Process Rev J 101:102711

    Article  Google Scholar 

  112. Ghaleb SAM, Vasanthi V (2020) Energy efficient multipath routing using multi-objective grey wolf optimizer based dynamic source routing algorithm for manet. Int J Adv Sci Technol 29(3):6096–6117

    Google Scholar 

  113. Farina M, Amato P (2004) A fuzzy definition of “optimality’’ for many-criteria optimization problems. IEEE Trans Syst Man Cybern A Syst Hum 34(3):315–326

    Article  Google Scholar 

  114. López Jaimes A, Coello Coello CA (2009) Some techniques to deal with many-objective problems. In: Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers, pp 2693–2696

  115. Farina M, Amato P (2003) Fuzzy optimality and evolutionary multiobjective optimization. In: International conference on evolutionary multi-criterion optimization. Springer, pp 58–72

  116. Khah SM, Mahboob AS, Shahbandegan S, Zahiri SH (2020) Optimal design of a low power, high speed one-bit full adder using multi-objective grey wolf optimizer. In: 6th Iranian conference on signal processing and intelligent systems, ICSPIS 2020

  117. Sasikumar A, Rajappa M (2017) A design of low power transconductance operational amplifier using multi-objective grey wolf optimization technique. Int J Mech Eng Technol 8(8):334–340

    Google Scholar 

  118. Premkumar M, Jangir P, Kumar BS, Alqudah MA, Nisar KS (2022) Multi-objective grey wolf optimization algorithm for solving real-world BLDC motor design problem. Comput Mater Continua 70(2):2435–2452

    Article  Google Scholar 

  119. Mahanta GB, Rout A, Gunji B, Deepak BBVL, Biswal BB (2020) Multi-objective design optimization of a bioinspired underactuated robotic gripper using multi-objective grey wolf optimizer. In: Lecture notes in mechanical engineering, pp 1497–1509

  120. Ling Q, Zhang Q, Wei Y, Kong L, Zhu L (2021) Slope reliability evaluation based on multi-objective grey wolf optimization-multi-kernel-based extreme learning machine agent model. Bull Eng Geol Env 80(3):2011–2024

    Article  Google Scholar 

  121. Saini N, Saha S, Jangra A, Bhattacharyya P (2019) Extractive single document summarization using multi-objective optimization: exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm. Knowl-Based Syst 164:45–67

    Article  Google Scholar 

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Authors and Affiliations

  1. Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman, UAE

    Sharif Naser Makhadmeh & Mohammed Azmi Al-Betar

  2. MLALP Research Group, University of Sharjah, Sharjah, UAE

    Osama Ahmad Alomari

  3. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Fortitude Valley, Brisbane, QLD, 4006, Australia

    Seyedali Mirjalili

  4. Yonsei Frontier Lab, Yonsei University, Seoul, Republic of Korea

    Seyedali Mirjalili

  5. Department of Information Technology, Al-Huson University College, Al-Balqa Applied University, Al-Huson, P.O. Box 50, Irbid, Jordan

    Mohammed Azmi Al-Betar

  6. Department of Computer Science, University of Sharjah, Sharjah, UAE

    Ashraf Elnagar

Authors
  1. Sharif Naser Makhadmeh
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  2. Osama Ahmad Alomari
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  3. Seyedali Mirjalili
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  4. Mohammed Azmi Al-Betar
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  5. Ashraf Elnagar
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Correspondence to Sharif Naser Makhadmeh.

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Makhadmeh, S.N., Alomari, O.A., Mirjalili, S. et al. Recent advances in multi-objective grey wolf optimizer, its versions and applications. Neural Comput & Applic 34, 19723–19749 (2022). https://doi.org/10.1007/s00521-022-07704-5

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