A Quasi-Oppositional Based Flamingo Search Algorithm Integrated with Generalized Ring Crossover for Effective Feature Selection

References

• R. P. Agrawal, H. F. Abutarboush, T. Ganesh, and A. W. Mohamed, “Metaheuristic algorithms on feature selection: A survey of One decade of research (2009–2019),” IEEE Access., Vol. 9, pp. 26766–26791, 2021.
   Web of Science ®Google Scholar
• S. Rathee, and S. Ratnoo, “Feature selection using multi-objective CHC genetic algorithm,” Proc. Comput. Sci.,Vol. 167, pp. 1656–1664, 2020.
   Google Scholar
• M. Sharma, and P. Kaur, “A comprehensive analysis of nature-inspired meta-heuristic techniques for feature selection problem,” Arch. Comput. Methods Eng., Vol. 28, pp. 1103–1127, 2021.
   Web of Science ®Google Scholar
• L. Abualigah, and A. J. Dulaimi, “A novel feature selection method for data mining tasks using hybrid Sine Cosine Algorithm and Genetic Algorithm,” Cluster. Comput., Vol. 24, pp. 2161–2176, 2021.
   Web of Science ®Google Scholar
• S. Arora, and P. Anand, “Binary butterfly optimization approaches for feature selection,” Expert. Syst. Appl.,Vol. 116, pp. 147–160, 2019. doi:10.1016/j.eswa.2018.08.051.
   Web of Science ®Google Scholar
• C. B. Gokulnath, and S. P. Shantharajah, “An optimized feature selection based on genetic approach and support vector machine for heart disease,” Clust. Comput., Vol. 22, no. 6, pp. 14777–14787, 2019.
   Google Scholar
• N. Mallenahalli, and T. H. Sarma. “A tunable particle swarm size optimization algorithm for feature selection,” In: 2018 IEEE congress on evolutionary computation. IEEE, 2018.
   Google Scholar
• M. Yan, and W. Luo, “A hybrid algorithm based on binary chemical reaction optimization and tabu search for feature selection of high-dimensional biomedical data,” Tsinghua Sci. Technol., Vol. 23, no. 6, pp. 733–743, 2018.
   Web of Science ®Google Scholar
• G. I. Sayed, and G. Khoriba. A novel chaotic salp swarm algorithm for global optimization and feature selection. New York: Springer, 2018.
   Google Scholar
• L. M. Q. Abualigah, “Feature selection and enhanced krill herd algorithm for text document clustering,” in Studies in computational intelligence, Laith Mohammad Qasim Abualigah, Ed. Berlin: Springer, 2019, pp. 1–65.
   Google Scholar
• L. M. Abualigah, A. T. Khader, and E. S. Hanandeh, “A new feature selection method to improve the document clustering using particle swarm optimization algorithm,” J. Comput. Sci., Vol. 25, pp. 456–466, 2018.
   Web of Science ®Google Scholar
• H. Chen, Y. Hou, Q. Luo, Z. Hu, and L. Yan, “Text feature selection based on water wave optimization algorithm,” In: International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2018, pp. 546–551.
   Google Scholar
• S. Rahnamayan, H. R. Tizhoosh, and M. M. A. Salama, “Quasioppositional differential evolution,” Proc. IEEE Congr. Evol. Comput., Vol. 4424748, pp. 2229–2236, Sep. 2007.
   Google Scholar
• R. Liu, T. Wang, J. Zhou, X. Hao, Y. Xu, and A. J. Qiu, “Improved African vulture optimization algorithm based on quasi-oppositional differential evolution operator,” IEEE Access., Vol. 10, pp. 95197–95218, 2022.
   Web of Science ®Google Scholar
• J. Xing, H. Zhao, H. Chen, R. Deng, and L. Xiao, “Boosting whale optimizer with quasi-oppositional learning and Gaussian barebone for feature selection and COVID-19 image segmentation,” J. Bionic Eng., Vol. 20, pp. 797–818, 2023.
   PubMed Web of Science ®Google Scholar
• L. Abualigah, A. J. A. Aldulaimi, M. A. Shinwan, and M. Shehab, “A proposed hybrid feature selection method for data mining tasks,” Int. J. Sci. Appl. Inf. Technol., Vol. 8, no. 6, pp. 139–143, 2019.
   Google Scholar
• W. Zhiheng, and L. Jianhu, “Flamingo search algorithm: A new swarm intelligence optimization algorithm,” IEEE Explore, Vol. 9, pp. 88564–885582, 2021.
   Google Scholar
• A. Pandey, and A. Jain, “Comparative analysis of KNN algorithm using various normalization techniques,” Int. J. Comp. Netw. Inform. Sec., Vol. 9, pp. 36–42, 2017.
   Google Scholar
• D. Dua, and T. E. Karra. “UCI machine learning repository,” Ph.D. dissertation, Irvine, SchoolInf. Comput. Sci., Univ. California, Oakland, CA, USA, 2017.
   Google Scholar
• W. Zhao, S. Han, W. Meng, and D. Sun, “Bsdp: Big sensor data preprocessing in multi-source fusion positioning system using compressive sensing,” IEEE Trans. Veh. Technol., Vol. 68, no. 9, pp. 8866–8880, 2019.
   Web of Science ®Google Scholar
• E. Emary, and H. M. Zawbaa, “Feature selection via lèvy antlion optimization,” Patt. Anal. Appl., Vol. 22, no. 3, pp. 857–876, Aug. 2019.
   Web of Science ®Google Scholar
• Y. Shia, P. Lie, H. Yuan, J. Miao, and L. Niua, “Fast kernel extreme learning machine For ordinal regression,” Knowl. Based. Syst., Vol. 177, pp. 44–54, 2019.
   Web of Science ®Google Scholar
• S. Rimcharoen, and N. Leelathakul, “Ring-based crossovers in genetic algorithms: characteristic decomposition and their generalization,” IEEE. Access., Vol. 9, pp. 137902–137922, 2021.
   Web of Science ®Google Scholar