A proposed Hyper-Heuristic optimizer Nesting Grey Wolf Optimizer and COOT Algorithm for Multilevel Task




  • Afrah U. Mosaa Informatics Institute for Postgraduate Studies, Iraqi Commission for Computers and Informatics, Baghdad, Iraq,
  • Waleed A. Mahmoud Al-Jawher College of Engineering Uruk University, Baghdad, Iraq

It can be extremely difficult to find the optimal solution in many complex optimization problems. The goal of optimization algorithms in such cases is to locate a feasible solution that is as close as possible to the optimal one. These algorithms are called metaheuristic optimization algorithms and the majority of them take their inspiration from nature and work to solve challenging problems in a variety of fields. In this paper, a combination between GWO and Coot algorithm was proposed. The effectiveness of the GWO algorithm has been demonstrated in many fields, including engineering and medicine. However, GWO has a disadvantage: the potential to enter the local minima due to a lack of diversity. GWO and the Coot algorithm were merged to fix this flaw. Ten benchmark functions were used to evaluate the performance of this hybrid technique, and its results were compared to those of other common optimization algorithms, including GWO, Cuckoo Search (CS), and the Shuffled Frog Leaping algorithm (SFLA). The results show that the suggested algorithm can provide results that are both competitive and more consistent than the other algorithms in most test functions.


grey wolf optimization , optimization, metaheuristic, Coot algorithm, , hybrid algorithm

[1] A. H. Halim and I. I. Swagatam, Performance assessment of the metaheuristic optimization algorithms : an exhaustive review, vol. 54, no. 3. Springer Netherlands, 2021. doi: 10.1007/s10462-020-09906-6.

[2] L. Bottou, F. E. Curtis, and J. Nocedal, “Optimization Methods for Large-Scale Machine Learning,” SIAM Rev., vol. 60, no. 2, pp. 223–311, Jan. 2018, doi: 10.1137/16M1080173.

[3] M. Abd Elaziz et al., “Advanced metaheuristic optimization techniques in applications of deep neural networks: a review,” Neural Comput. Appl., vol. 33, no. 21, pp. 14079–14099, Nov. 2021, doi: 10.1007/s00521-021-05960-5.

[4] B. Arandian, A. Iraji, H. Alaei, S. Keawsawasvong, and M. L. Nehdi, “White-Tailed Eagle Algorithm for Global Optimization and Low-Cost and Low-CO2 Emission Design of Retaining Structures,” Sustainability, vol. 14, no. 17, p. 10673, Aug. 2022, doi: 10.3390/su141710673.

[5] J. H. Holland, “Genetic Algorithms,” Sci. Am., vol. 267, no. 1, pp. 66–72, Jul. 1992, doi: 10.1038/scientificamerican0792-66.

[6] M. Dorigo, M. Birattari, and T. Stutzle, “Ant colony optimization,” IEEE Comput. Intell. Mag., vol. 1, no. 4, pp. 28–39, Nov. 2006, doi: 10.1109/MCI.2006.329691.

[7] D. Karaboga and B. Basturk, “Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems,” in Foundations of Fuzzy Logic and Soft Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 789–798. doi: 10.1007/978-3-540-72950-1_77.

[8] X.-S. Yang and Suash Deb, “Cuckoo Search via Lévy flights,” in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), IEEE, 2009, pp. 210–214. doi: 10.1109/NABIC.2009.5393690.

[9] P. S. Shelokar, P. Siarry, V. K. Jayaraman, and B. D. Kulkarni, “Particle swarm and ant colony algorithms hybridized for improved continuous optimization,” Appl. Math. Comput., vol. 188, no. 1, pp. 129–142, May 2007, doi: 10.1016/j.amc.2006.09.098.

[10] A. Bouaouda and Y. Sayouti, “Hybrid Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy System: A Review of the State-of-the-Art,” Arch. Comput. Methods Eng., vol. 29, no. 6, pp. 4049–4083, Oct. 2022, doi: 10.1007/s11831-022-09730-x.

[11] Y. Meraihi, A. B. Gabis, S. Mirjalili, and A. Ramdane-Cherif, “Grasshopper Optimization Algorithm: Theory, Variants, and Applications,” IEEE Access, vol. 9, pp. 50001–50024, 2021, doi: 10.1109/ACCESS.2021.3067597.

[12] J. Li, H. Lei, A. H. Alavi, and G.-G. Wang, “Elephant Herding Optimization: Variants, Hybrids, and Applications,” Mathematics, vol. 8, no. 9, p. 1415, Aug. 2020, doi: 10.3390/math8091415.

[13] P. Hoseini and M. G. Shayesteh, “Hybrid Ant Colony Optimization, Genetic Algorithm, and Simulated Annealing for image contrast enhancement,” in IEEE Congress on Evolutionary Computation, IEEE, Jul. 2010, pp. 1–6. doi: 10.1109/CEC.2010.5586542.

[14] I. Ciornei and E. Kyriakides, “Hybrid Ant Colony-Genetic Algorithm (GAAPI) for Global Continuous Optimization,” IEEE Trans. Syst. Man, Cybern. Part B, vol. 42, no. 1, pp. 234–245, Feb. 2012, doi: 10.1109/TSMCB.2011.2164245.

[15] S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,” Adv. Eng. Softw., vol. 69, pp. 46–61, 2014, doi: 10.1016/j.advengsoft.2013.12.007.

[16] H. U. Ahmed, R. R. Mostafa, A. Mohammed, P. Sihag, and A. Qadir, “Support vector regression (SVR) and grey wolf optimization (GWO) to predict the compressive strength of GGBFS-based geopolymer concrete,” Neural Comput. Appl., vol. 35, no. 3, pp. 2909–2926, Jan. 2023, doi: 10.1007/s00521-022-07724-1.

[17] M. Banaie-Dezfouli, M. H. Nadimi-Shahraki, and Z. Beheshti, “R-GWO: Representative-based grey wolf optimizer for solving engineering problems,” Appl. Soft Comput., vol. 106, p. 107328, Jul. 2021, doi: 10.1016/j.asoc.2021.107328.

[18] M. Ghalambaz, R. Jalilzadeh Yengejeh, and A. H. Davami, “Building energy optimization using Grey Wolf Optimizer (GWO),” Case Stud. Therm. Eng., vol. 27, p. 101250, Oct. 2021, doi: 10.1016/j.csite.2021.101250.

[19] X. Zhang, X. Wang, H. Chen, D. Wang, and Z. Fu, “Improved GWO for large-scale function optimization and MLP optimization in cancer identification,” Neural Comput. Appl., vol. 32, no. 5, pp. 1305–1325, Mar. 2020, doi: 10.1007/s00521-019-04483-4.

[20] A. A. Heidari and P. Pahlavani, “An efficient modified grey wolf optimizer with Lévy flight for optimization tasks,” Appl. Soft Comput., vol. 60, pp. 115–134, Nov. 2017, doi:10.1016/j.asoc.2017.06.044 .

Mosaa, A. U. ., & Al-Jawher, W. A. M. . (2023). A proposed Hyper-Heuristic optimizer Nesting Grey Wolf Optimizer and COOT Algorithm for Multilevel Task. Journal Port Science Research, 6(4), 310–317. https://doi.org/10.36371/port.2023.4.1


Download data is not yet available.

Most read articles by the same author(s)