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

Year 2022, Volume: 35 Issue: 2, 485 - 504, 01.06.2022
https://doi.org/10.35378/gujs.820885

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. 

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Thanks

This research work has no funding.

References

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Year 2022, Volume: 35 Issue: 2, 485 - 504, 01.06.2022
https://doi.org/10.35378/gujs.820885

Abstract

Project Number

None

References

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  • [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).
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  • [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).
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  • [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).
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  • [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
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There are 80 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Computer Engineering
Authors

Emmanuel Dada 0000-0002-1132-5447

Stephen Joseph 0000-0001-5701-2633

David Oyewola 0000-0001-9638-8764

Alaba Ayotunde Fadele 0000-0002-1125-0780

Haruna Chiroma 0000-0003-3446-4316

Shafi'i Muhammad Abdulhamid 0000-0001-9196-9447

Project Number None
Publication Date June 1, 2022
Published in Issue Year 2022 Volume: 35 Issue: 2

Cite

APA Dada, E., Joseph, S., Oyewola, D., Fadele, A. A., et al. (2022). Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons. Gazi University Journal of Science, 35(2), 485-504. https://doi.org/10.35378/gujs.820885
AMA Dada E, Joseph S, Oyewola D, Fadele AA, Chiroma H, Abdulhamid SM. Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons. Gazi University Journal of Science. June 2022;35(2):485-504. doi:10.35378/gujs.820885
Chicago Dada, Emmanuel, Stephen Joseph, David Oyewola, Alaba Ayotunde Fadele, Haruna Chiroma, and Shafi’i Muhammad Abdulhamid. “Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons”. Gazi University Journal of Science 35, no. 2 (June 2022): 485-504. https://doi.org/10.35378/gujs.820885.
EndNote Dada E, Joseph S, Oyewola D, Fadele AA, Chiroma H, Abdulhamid SM (June 1, 2022) Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons. Gazi University Journal of Science 35 2 485–504.
IEEE E. Dada, S. Joseph, D. Oyewola, A. A. Fadele, H. Chiroma, and S. M. Abdulhamid, “Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons”, Gazi University Journal of Science, vol. 35, no. 2, pp. 485–504, 2022, doi: 10.35378/gujs.820885.
ISNAD Dada, Emmanuel et al. “Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons”. Gazi University Journal of Science 35/2 (June 2022), 485-504. https://doi.org/10.35378/gujs.820885.
JAMA Dada E, Joseph S, Oyewola D, Fadele AA, Chiroma H, Abdulhamid SM. Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons. Gazi University Journal of Science. 2022;35:485–504.
MLA Dada, Emmanuel et al. “Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons”. Gazi University Journal of Science, vol. 35, no. 2, 2022, pp. 485-04, doi:10.35378/gujs.820885.
Vancouver Dada E, Joseph S, Oyewola D, Fadele AA, Chiroma H, Abdulhamid SM. Application of Grey Wolf Optimization Algorithm: Recent Trends, Issues, and Possible Horizons. Gazi University Journal of Science. 2022;35(2):485-504.

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