Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons

Year 2022, Volume: 35 Issue: 2, 485 - 504, 01.06.2022

Emmanuel Dada , Stephen Joseph , David Oyewola , Alaba Ayotunde Fadele , Haruna Chiroma , Shafi'i Muhammad Abdulhamid

https://doi.org/10.35378/gujs.820885

Cited By: 18

Abstract

The development of Grey Wolf Optimisation (GWO) Algorithm was motivated by the biological behaviours of swarm of wolves hunting for prey. This paper presents recent progress on Grey Wolf Optimization (GWO) algorithm, its variants and their applications, issues, and likely prospects. The review revealed that opportunities still exists for development of more robust and stable variants of GWO that will overcome the shortcomings of existing variants. This review has the potential to stimulate researchers in the area of nature-inspired algorithms to further advance the effectiveness of the GWO and its ability to solve problems. Such problems can be real-life, complicated and nonlinear optimization problems in different domain of human endeavour. Suggestions for new research directions that have the capacity to increase the performance of GWO are presented. It is expected that this paper will serve as reading material for beginners whereas experienced researchers can also use it as an article yardstick for further development of GWO algorithms. 

Keywords

Grey wolf optimisation, Single objective gwo, Multi-objective gwo, Hybrid gwo

Supporting Institution

None

Project Number

None

Thanks

This research work has no funding.

References

• [1] Rezaei, H., Bozorg-Haddad, O., Chu, X., “Grey wolf optimization (GWO) algorithm”, In Advanced Optimization by Nature-Inspired Algorithms, Springer, Singapore, 81-91, (2018).
• [2] Kennedy, J., Eberhart, R., “Particle swarm optimization”, In Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, 4:1942-1948, (1995).
• [3] Karaboga, D., Basturk, B., “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm”, Journal of Global Optimization, 39(3): 459-471, (2007).
• [4] Yang, X. S., “A new metaheuristic bat-inspired algorithm”, In: Gonzalez et al. Nature Inspired Cooperative Strategies for Optimization, 284, 65–74, (2010).
• [5] Pham, D. T., Ghanbarzadeh, A., Koç, E., Otri, S., Rahim, S., Zaidi, M., “The bees algorithm - a novel tool for complex optimisation problems”, In Intelligent production machines and systems, Elsevier Science Ltd, 454-459, (2006).
• [6] Mucherino, A., Seref, O., “Monkey search: a novel metaheuristic search for global optimization”, In AIP conference proceedings, American Institute of Physics, 953(1): 162-173, (2007).
• [7] Krishnanand, K. N., Ghose, D., “Detection of multiple source locations using a glowworm metaphor with applications to collective robotics”, In Proceedings 2005 IEEE Swarm Intelligence Symposium, SIS 2005, IEEE, 84-91, (2005).
• [8] Passino, K. M., “Biomimicry of bacterial foraging for distributed optimization and control”, Control Systems, IEEE, 3, 52–67, (2002).
• [9] Li, X. L., “An optimizing method based on autonomous animats: fish-swarm algorithm”, Systems Engineering-Theory and Practice, 22(11): 32-38, (2002).
• [10] Chu, S. A., Tsai, P. W., Pan, J. S., “Cat swarm optimization”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4099: LNAI: 854–858, (2006).
• [11] Fister, Jr. I., Yang, X. S., Fister, I., Brest, J., Fister, D., “A brief review of nature-inspired algorithms for optimization”, arXiv preprint arXiv:1307.4186, (2013).
• [12] Yang, X. S., “Flower pollination algorithm for global optimization”, In International conference on unconventional computing and natural computation, Springer, Berlin, Heidelberg, 240-249, (2012).
• [13] Meng, X., Liu, Y., Gao, X., Zhang, H., “A new bio-inspired algorithm: chicken swarm optimization”, In International conference in swarm intelligence, Springer, Cham, 86-94, (2014).
• [14] Jiang, H., Zhang, S., Ren, Z., Lai, X., Piao, Y., “Approximate muscle guided beam search for three-index assignment problem”, In International Conference in Swarm Intelligence, Springer, Cham., 44-52, (2014).
• [15] Mo, H., Liu, L., Geng, M., “A magnetotactic bacteria algorithm based on power spectrum for optimization”, In International Conference in Swarm Intelligence, Springer, Cham, 115-125, (2014).
• [16] Wang, G. G., Deb, S., Coelho, L. D., “Elephant herding optimization”, In 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI) 2015 Dec 7, IEEE, 1-5, (2015).
• [17] Findik, O., “Bull optimization algorithm based on genetic operators for continuous optimization problems”, Turkish Journal of Electrical Engineering and Computer Sciences, 23 (Sup. 1): 2225-39, (2015). doi:10.3906/elk-1307-123
• [18] Mirjalili, S., Mirjalili, S. M., Lewis, A., “Grey wolf optimizer”, Advances in Engineering Software, 69, 46-61, (2014).
• [19] Gholizadeh, S., “Optimal design of double layer grids considering nonlinear behaviour by sequential grey wolf algorithm”. Iran University of Science and Technology, 5(4), 511-523, (2015).
• [20] Mirjalili, S., “How effective is the Grey Wolf optimizer in training multi-layer perceptrons”, Applied Intelligence, 43(1), 150-161, (2015).
• [21] Saremi, S., Mirjalili, S. Z., Mirjalili, S. M., “Evolutionary population dynamics and grey wolf optimizer”, Neural Computing and Applications, 26(5), 1257-1263, (2015).
• [22] Sulaiman, M. H., Mustaffa, Z., Mohamed, M. R., Aliman, O., “Using the gray wolf optimizer for solving optimal reactive power dispatch problem”, Applied Soft Computing, 32, 286-292, (2015).
• [23] El-Fergany, A. A., Hasanien, H. M., “Single and multi-objective optimal power flow using grey wolf optimizer and differential evolution algorithms”, Electric Power Components and Systems, 43(13), 1548-1559, (2015).
• [24] Madadi, A., Motlagh, M. M., “Optimal control of DC motor using grey wolf optimizer algorithm”, Technical Journal of Engineering and Applied Science, 4(4), 373-379, (2014).
• [25] Guha, D., Roy, P. K., Banerjee, S., “Load frequency control of interconnected power system using grey wolf optimization”, Swarm and Evolutionary Computation, 27, 97-115, (2016).
• [26] Song, X., Tang, L., Zhao, S., Zhang, X., Li, L., Huang, J., Cai, W., “Grey wolf optimizer for parameter estimation in surface waves”, Soil Dynamics and Earthquake Engineering, 75: 147-157, (2015). http://dx.doi.org/10.1016/j.soildyn.2015.04.004
• [27] Faris, H., Aljarah, I., Al-Betar, M. A., Mirjalili, S., “Grey wolf optimizer: a review of recent variants and applications”, Neural Computing and Applications. 30(2): 413-35, (2018).
• [28] Hatta, N. M., Zain, A. M., Sallehuddin, R., Shayfull, Z., Yusoff, Y., “Recent studies on optimisation method of Grey Wolf Optimiser (GWO): a review (2014–2017)”, Artificial Intelligence Review, 52(4): 2651-2683, (2018).
• [29] Panda, M., Das, B. “Grey Wolf Optimizer and Its Applications: A Survey”, In Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems, Springer, Singapore, 179-194, (2019). https://doi.org/10.1007/978-981-13-7091-5_17
• [30] Al-Tashi, Q., Rais, H. M., Abdulkadir, S. J., Mirjalili, S., Alhussian, H., “A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classification”, In Evolutionary Machine Learning Techniques, Springer, Singapore, 273-286, (2020). https://doi.org/10.1007/978-981-32-9990-0_13
• [31] Negi, G., Kumar, A., Pant, S., Ram, M., “GWO: a review and applications”, International Journal of System Assurance Engineering and Management, 1-8, (2020). https://doi.org/10.1007/s13198-020-00995-8
• [32] Yang, B., Zhang, X., Yu, T., Shu, H., Fang, Z., “Grouped grey wolf optimizer for maximum power point tracking of doubly-fed induction generator based wind turbine”, Energy Conversion and Management. 133: 427-43, (2017). http://dx.doi.org/10.1016/j.enconman.2016.10.062
• [33] Lal, D. K., Barisal, A. K., Tripathy, M., “Grey wolf optimizer algorithm based Fuzzy PID controller for AGC of multi-area power system with TCPS”, Procedia Computer Science, 92: 99-105, (2016). doi: 10.1016/j.procs.2016.07.329
• [34] Precup, R. E., David, R. C., Petriu, E. M., Szedlak-Stinean, A. I., Bojan-Dragos, C. A., “Grey wolf optimizer-based approach to the tuning of pi-fuzzy controllers with a reduced process parametric sensitivity”, IFAC – PapersOnLine, 49(5): 55-60, (2016). 10.1016/j.ifacol.2016.07.089
• [35] Debnath, M. K., Mallick, R. K., Sahu, B. K., “Application of hybrid differential evolution–grey wolf optimization algorithm for automatic generation control of a multi-source interconnected power system using optimal fuzzy–PID controller”, Electric Power Components and Systems. 45(19): 2104-17, (2017). DOI: 10.1080/15325008.2017.1402221.
• [36] Lu, C., Xiao, S., Li, X., Gao, L., “An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production”, Advances in Engineering Software, 99, 161-176, (2016). http://dx.doi.org/10.1016/j.advengsoft.2016.06.004
• [37] Panwar, L. K., Reddy, S., Verma, A., Panigrahi, B. K., Kumar, R., “Binary grey wolf optimizer for large scale unit commitment problem”, Swarm and Evolutionary Computation, 38: 251-66, (2018). http://dx.doi.org/10.1016/j.swevo.2017.08.002
• [38] Komaki, G. M., Kayvanfar, V., “Grey Wolf Optimizer algorithm for the two-stage assembly flow shop scheduling problem with release time”, Journal of Computational Science, 8: 109-20, (2015). http://dx.doi.org/10.1016/j.jocs.2015.03.011
• [39] Kamboj, V. K., “A novel hybrid PSO–GWO approach for unit commitment problem”, Neural Computing and Applications, 27(6): 1643-55, (2016).
• [40] Ab Rashid, M. F. F., “A hybrid Ant-Wolf Algorithm to optimize assembly sequence planning problem”, Assembly Automation, (2017).
• [41] Zhang, S., Zhou, Y., Li, Z., Pan, W., “Grey wolf optimizer for unmanned combat aerial vehicle path planning”, Advances in Engineering Software, 99, 121-136, (2016). http://dx.doi.org/10.1016/j.advengsoft.2016.05.015
• [42] Jain, U., Tiwari, R., Godfrey, W. W., “Odor source localization by concatenating particle swarm optimization and Grey Wolf optimizer”, In Advanced Computational and Communication Paradigms, Springer, Singapore, 145-153, (2018).
• [43] Pradhan, M., Roy, P. K., Pal, T., “Grey wolf optimization applied to economic load dispatch problems”, International Journal of Electrical Power and Energy Systems, 83: 325-34, (2016). http://dx.doi.org/10.1016/j.ijepes.2016.04.034
• [44] Pradhan, M., Roy, P. K., Pal, T., “Oppositional based grey wolf optimization algorithm for economic dispatch problem of power system”, Ain Shams Engineering Journal, 9(4): 2015-25, (2018). http://dx.doi.org/10.1016/j.asej.2016.08.023
• [45] Jayabarathi, T., Raghunathan, T., Adarsh, B. R., Suganthan, P. N., “Economic dispatch using hybrid grey wolf optimizer”, Energy, 111: 630-41, (2016). http://dx.doi.org/10.1016/j.energy.2016.05.105
• [46] Chopra, N., Kumar, G., Mehta, S., “Hybrid GWO-PSO algorithm for solving convex economic load dispatch problem”, International Journal of Research in Advent Technology , 4(6): 37-41, (2017).
• [47] Eid, H. F., Abraham, A., “Plant species identification using leaf biometrics and swarm optimization: A hybrid PSO, GWO, SVM model”, International Journal of Hybrid Intelligent Systems. 14(3): 155-65, (2017).
• [48] Vinothini, J., Bakkiyaraj, R. A., “Grey Wolf Optimization Algorithm for Colour Image Enhancement Considering Brightness Preservation Constraint”, International Journal of Emerging Trends in Science and Technology, 03(05): 4049-4055, (2015). DOI: http://dx.doi.org/10.18535/ijetst/v3i05.28
• [49] Lakshminarayanan, S., “Nature inspired grey wolf optimizer algorithm for minimizing operating cost in green smart home”, Doctoral Dissertation, University of Toledo, (2015).
• [50] Medjahed, S. A., Saadi, T. A., Benyettou, A., Ouali, M., “Gray wolf optimizer for hyperspectral band selection”, Applied Soft Computing, 40: 178-86, (2016). http://dx.doi.org/10.1016/j.asoc.2015.09.045
• [51] Mirjalili, S., Saremi, S., Mirjalili, S. M., Coelho, L. D. S., “Multi-objective grey wolf optimizer: a novel algorithm for multi-criterion optimization”, Expert Systems with Applications, 47, 106-119, (2016). DOI http://dx.doi.org/10.1016/j.eswa.2015.10.039
• [52] Kohli, M., Arora, S., “Chaotic grey wolf optimization algorithm for constrained optimization problems”, Journal of Computational Design and Engineering, 5(4): 458-72, (2018). http://dx.doi.org/10.1016/j.jcde.2017.02.005
• [53] Singh, N., Singh, S. B., “A novel hybrid GWO-SCA approach for optimization problems”, Engineering Science and Technology, an International Journal, 20(6): 1586-601, (2017). https://doi.org/10.1016/j.jestch.2017.11.001
• [54] Chandra, M., Agrawal, A., Kishor, A., Niyogi, R., “Web service selection with global constraints using modified gray wolf optimizer”, In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 1989–1994, (2016).
• [55] Singh, N., Singh, S. B., “Hybrid algorithm of particle swarm optimization and grey wolf optimizer for improving convergence performance”, Journal of Applied Mathematics, (2017).
• [56] ElGayyar, M., Emary, E., Sweilam, N. H., Abdelazeem, M., “A hybrid Grey Wolf-bat algorithm for global optimization”, In International Conference on Advanced Machine Learning Technologies and Applications, Springer, Cham., 3-12, (2018)
• [57] Heidari, A. A., Pahlavani, P., “An efficient modified grey wolf optimizer with Lévy flight for optimization tasks”, Applied Soft Computing, 60: 115-34, (2017). http://dx.doi.org/10.1016/j.asoc.2017.06.044
• [58] Jitkongchuen, D., Phaidang, P., Pongtawevirat, P., “Grey wolf optimization algorithm with invasion-based migration operation”, In2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS), IEEE, 1-5, (2016).
• [59] Pan, J. S., Dao, T. K., Chu, S. C., “A novel hybrid GWO-FPA algorithm for optimization applications”, In International Conference on Smart Vehicular Technology, Transportation, Communication and Applications, Springer, Cham., 274-281, (2017)
• [60] Zhang, X., Kang, Q., Cheng, J., Wang, X., “A novel hybrid algorithm based on biogeography-based optimization and grey wolf optimizer”, Applied Soft Computing. 67: 197-214, (2018). https://doi.org/doi:10.1016/j.asoc.2018.02.049
• [61] Tawhid, M. A., Ali, A. F., “A hybrid grey wolf optimizer and genetic algorithm for minimizing potential energy function”, Memetic Computing, 9(4): 347-59, (2017).
• [62] Jeet, K., “Grey wolf algorithm for software organization”, Indian Journal of Scientific Research, 7(2): 214-217, (2017).
• [63] Jadhav, A. N., Gomathi, N., “WGC: hybridization of exponential grey wolf optimizer with whale optimization for data clustering”, Alexandria Engineering Journal, 2018 Sep 1, 57(3): 1569-84, (2017). http://dx.doi.org/10.1016/j.aej.2017.04.013
• [64] Wang, M., Chen, H., Li, H., Cai, Z., Zhao, X., Tong, C., Li, J., Xu, X., “Grey wolf optimization evolving kernel extreme learning machine: Application to bankruptcy prediction”, Engineering Applications of Artificial Intelligence, 63: 54-68, (2017). http://dx.doi.org/10.1016/j.engappai.2017.05.003.
• [65] Emary, E., Yamany, W., Hassanien, A. E., Snasel, V., “Multi-objective gray-wolf optimization for attribute reduction”, Procedia Computer Science, January 2015, 1: 65: 623-32, (2015). http://dx.doi.org/10.1016/j.neucom.2015.06.083
• [66] Tu, Q., Chen, X. and Liu, X., “Multi-strategy ensemble grey wolf optimizer and its application to feature selection”, Applied Soft Computing, 76, 16-30, (2019). https://doi.org/10.1016/j.asoc.2018.11.047
• [67] Hu, P., Pan, J. S., Chu, S. C., Improved Binary Grey Wolf Optimizer and Its application for feature selection. Knowledge Based Systems, 105746, (2020). https://doi.org/10.1016/j.knosys.2020.105746
• [68] Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z. N., Tong, C., Liu, W., Tian, X., “An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis”, Computational and Mathematical Methods in Medicine, 2017: 15, (2017). https://doi.org/10.1155/2017/9512741
• [69] Jayapriya, J., Arock, M., “A parallel gwo technique for aligning multiple molecular sequences”, In: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, 210–215, (2015).
• [70] Mostafa, A., Fouad, A., Houseni, M., Allam, N., Hassanien, A. E., Hefny, H., Aslanishvili, I., “A hybrid grey wolf based segmentation with statistical image for ct liver images”, In: International Conference on Advanced Intelligent Systems and Informatics, Springer, 846–855, (2016).
• [71] Sahoo, A., Chandra, S., “Multi-objective grey wolf optimizer for improved cervix lesion classification”, Applied Soft Computing, 52: 64–80, (2017).
• [72] Elhariri, E., El-Bendary, N., Hassanien, A. E., “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, 297–307, (2016).
• [73] Diwan, P., Khan, M. R., “Energy efficient communication for WSNs using Grey-Wolf optimization algorithm”, International Journal Of Engineering And Computer Science, 5(12), (2016).
• [74] Editorial, “Hybrid learning machine”, Neurocomputing, 72: 2729–2730, (2009).
• [75] Niu, P., Niu, S., Chang, L., “The defect of the Grey Wolf optimization algorithm and its verification method”, Knowledge-Based Systems, 1; 171: 37-43, (2019).
• [76] Rodriguez, L., Castillo, O., Soria, J., “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, 371–390, (2017).
• [77] Mafarja, M., Eleyan, D., Abdullah, S., Mirjalili, S., “S-Shaped vs. V-Shaped Transfer Functions for Ant Lion Optimization Algorithm in Feature Selection Problem”, In Proceedings of International Conference on Future Networks and Distributed Systems, Cambridge, UK, July 2017, (ICFNDS) 2017, 6 pages, (2017). DOI:10.1145/3102304.3102325.
• [78] Hassani, Z., Hajihashemi, V., Borna, K., Sahraei Dehmajnoonie, I., “A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization”, Journal of Sciences, Islamic Republic of Iran, 31(2): 165-173 (2020).
• [79] Dada, E. G., Bassi, J. S., Chiroma, H., Adetunmbi, A. O.; Ajibuwa, O. E., “Machine learning for email spam filtering: review, Approaches and Open Research Problems”, Heliyon, 5(6), p.e01802 (2019).
• [80] Li, Q., Chen, H., Huang, H., Zhao, X., Cai, Z., Tong, C., Liu, W. and Tian, X., “An enhanced grey wolf optimization based feature selection wrapped kernel extreme learning machine for medical diagnosis”, Computational and Mathematical Methods in medicine, 2017.