Fuzzy logic-based Northern Goshawk algorithm optimization and hybridization of the Northern Goshawk and Black Widow algorithms
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1123Keywords:
The Northern Goshawk Optimization Algorithm (NGOA), The Black Widow Optimization Algorithm (BWOA), Fuzzy Logic (FL), Fuzzy Set (FS)Abstract
Innovations and new methods for solving long and difficult mathematical puzzles are essential to advances in many branches of research and knowledge. As a result, experts have proposed intelligent algorithms, which are determined by their ability to quickly and efficiently answer the most difficult mathematical puzzles. To achieve the greatest results in this worksheet, we used two different strategies. The first method involved integrating the Goshawk Optimization algorithm (NGOA) with fuzzy logic (FL), while the second method was based on two hybrids, the first by linking communities and the second by linking the equations between the Black Widow Optimization algorithm (BWOA) with the Northern Goshawk optimization algorithm (NGOA). Then we applied both techniques to the basic functions of ten algorithmic functions to get the results.
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References
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[2] B. Yang et al., “Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification,” Energy Convers. Manag., vol. 208, p. 112595, 2020.
[3] A. Kaveh and M. Khayatazad, “A new meta-heuristic method: ray optimization,” Comput. Struct., vol. 112, pp. 283–294, 2012.
[4] M. A. El Aziz, A. A. Ewees, and A. E. Hassanien, “Hybrid swarms optimization based image segmentation,” in Hybrid soft computing for image segmentation, Springer, 2016, pp. 1–21.
[5] J. Gryz and D. Krauze-Gryz, “Pigeon and poultry breeders, friends or enemies of the northern goshawk Accipiter gentilis? A long-term study of a population in Central Poland,” Animals, vol. 9, no. 4, p. 141, 2019.
[6] S. Rebollo, G. García-Salgado, L. Pérez-Camacho, S. Martínez-Hesterkamp, A. Navarro, and J.-M. Fernández-Pereira, “Prey preferences and recent changes in diet of a breeding population of the Northern Goshawk Accipiter gentilis in Southwestern Europe,” Bird Study, vol. 64, no. 4, pp. 464–475, 2017.
[7] J. W. Watson, D. W. Hays, and D. J. Pierce, “Efficacy of northern goshawk broadcast surveys in Washington state,” J. Wildl. Manage., pp. 98–106, 1999.
[8] M. Dehghani, Š. Hubálovský, and P. Trojovský, “Northern goshawk optimization: a new swarm-based algorithm for solving optimization problems,” IEEE Access, vol. 9, pp. 162059–162080, 2021.
[9] M. A. Ali, S. Kamel, M. H. Hassan, E. M. Ahmed, and M. Alanazi, “Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm,” Sustainability, vol. 14, no. 10, p. 6049, 2022.
[10] D. M. Donner, D. Anderson, D. Eklund, and M. St. Pierre, “Large‐scale forest composition influences northern goshawk nesting in Wisconsin,” J. Wildl. Manage., vol. 77, no. 3, pp. 495–504, 2013.
[11] G. Sutherland, “Northern Goshawk (Accipiter gentilis laingi) Habitat and Territory Models,” 2008.
[12] Y. A. Fattah Hamoodi and S. A. M Ramadhan, “Identification of Biometrics based on a Classical Mathematical Methods in Forensic Medicine.,” Indian J. Forensic Med. Toxicol., vol. 13, no. 3, 2019.
[13] S. Memar, A. Mahdavi-Meymand, and W. Sulisz, “Prediction of seasonal maximum wave height for unevenly spaced time series by Black Widow Optimization algorithm,” Mar. Struct., vol. 78, p. 103005, 2021.
[14] G. Hu, B. Du, X. Wang, and G. Wei, “An enhanced black widow optimization algorithm for feature selection,” Knowledge-Based Syst., vol. 235, p. 107638, 2022.
[15] V. Hayyolalam and A. A. P. Kazem, “Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems,” Eng. Appl. Artif. Intell., vol. 87, p. 103249, 2020.
[16] E. H. Houssein, B. E. Helmy, D. Oliva, A. A. Elngar, and H. Shaban, “A novel black widow optimization algorithm for multilevel thresholding image segmentation,” Expert Syst. Appl., vol. 167, p. 114159, 2021.
[17] R. Lowen, Fuzzy set theory: basic concepts, techniques and bibliography. Springer Science & Business Media, 2012.
[18] H.-J. Zimmermann, “Applications of fuzzy set theory to mathematical programming,” Inf. Sci. (Ny)., vol. 36, no. 1–2, pp. 29–58, 1985.
[19] L. A. Zadeh, “Fuzzy logic,” Computer (Long. Beach. Calif)., vol. 21, no. 4, pp. 83–93, 1988.
[20] F. K. Purian and E. Sadeghian, “Mobile robots path planning using ant colony optimization and Fuzzy Logic algorithms in unknown dynamic environments,” in 2013 International Conference on Control, Automation, Robotics and Embedded Systems (CARE), 2013, pp. 1–6.
[21] R. Ambigai and S. Prabhu, “Fuzzy logic algorithm based optimization of the tribological behavior of Al-Gr-Si3N4 hybrid composite,” Measurement, vol. 146, pp. 736–748, 2019.
[22] M. F. Hamza, H. J. Yap, and I. A. Choudhury, “Recent advances on the use of meta-heuristic optimization algorithms to optimize the type-2 fuzzy logic systems in intelligent control,” Neural Comput. Appl., vol. 28, no. 5, pp. 979–999, 2017.
[23] M. A. A. Alhafedh and O. S. Qasim, “Two-stage gene selection in microarray dataset using fuzzy mutual information and binary particle swarm optimization,” Indian J. Forensic Med. Toxicol., vol. 13, no. 4, pp. 1162–1171, 2019.
[24] A. M. Dalavi, A. Gomes, and A. J. Husain, “Bibliometric analysis of nature inspired optimization techniques,” Comput. Ind. Eng., vol. 169, p. 108161, 2022.
[25] Y. Maegawa et al., “A new survey method using convolutional neural networks for automatic classification of bird calls,” Ecol. Inform., vol. 61, p. 101164, 2021.
[26] G. Hu, B. Du, and X. Wang, “An improved black widow optimization algorithm for surfaces conversion,” Appl. Intell., pp. 1–42, 2022.
[27] M. Nanjappan, G. Natesan, and P. Krishnadoss, “An adaptive neuro-fuzzy inference system and black widow optimization approach for optimal resource utilization and task scheduling in a cloud environment,” Wirel. Pers. Commun., vol. 121, no. 3, pp. 1891–1916, 2021.
[28] A. T. H. Al-Rahlawee and J. Rahebi, “Multilevel thresholding of images with improved Otsu thresholding by black widow optimization algorithm,” Multimed. Tools Appl., vol. 80, no. 18, pp. 28217–28243, 2021.
[29] M. Azizi, R. G. Ejlali, S. A. M. Ghasemi, and S. Talatahari, “Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure,” Eng. Struct., vol. 192, pp. 53–70, 2019.
[30] Yahia, W. B., Al-Neama, M. W., & Arif, G. E. (2020). A Hybrid Optimization Algorithm of Ant Colony Search and NeighbourJoining Method to Solve the Travelling Salesman Problem. Advanced Mathematical Models and Applications, 5(1), 95-110.
[31] Younis, Y. S., Ali, A. H., Alhafidhb, O. K., Yahia, W. B., Alazzam, M. B., Hamad, A. A., & Meraf, Z. (2022). Early diagnosis of breast cancer using image processing techniques. Journal of Nanomaterials, 2022.
[32] X.-S. Yang, “Test problems in optimization,” arXiv Prepr. arXiv1008.0549, 2010.
[33] S. Arora and S. Singh, “Butterfly algorithm with levy flights for global optimization,” in 2015 International conference on signal processing, computing and control (ISPCC), 2015, pp. 220–224.
[34] X.-S. Yang and S. Deb, “Engineering optimisation by cuckoo search,” arXiv Prepr. arXiv1005.2908, 2010.
[35] X.-S. Yang, “Firefly algorithm, stochastic test functions and design optimisation,” arXiv Prepr. arXiv1003.1409, 2010.
[36] Yahia, Warif B., Mohammed W. Al-Neama, and G. E. Arif. "PNACO: parallel algorithm for neighbour joining hybridized with ant colony optimization on multi-core system." Вестник Южно-Уральского государственного университета. Серия: Математическое моделирование и программирование 13.4 (2020): 107-118.
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Published
2022-12-31
How to Cite
Alhafedh, M. A., & Mitras, B. A. (2022). Fuzzy logic-based Northern Goshawk algorithm optimization and hybridization of the Northern Goshawk and Black Widow algorithms. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Math Page 136–150. https://doi.org/10.29304/jqcm.2022.14.4.1123
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