Inversed Control Parameter in Whale Optimization Algorithm and Grey Wolf Optimizer for Wrapper-based Feature Selection: A comparative study

Li Yu Yab - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Noorhaniza Wahid - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia
Rahayu A Hamid - Universiti Tun Hussein Onn Malaysia, Parit Raja, Johor, Malaysia

Citation Format:



Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO) are well-perform metaheuristic algorithms used by various researchers in solving feature selection problems. Yet, the slow convergence speed issue in Whale Optimization Algorithm and Grey Wolf Optimizer could demote the performance of feature selection and classification accuracy. Therefore, to overcome this issue, a modified WOA (mWOA) and modified GWO (mGWO) for wrapper-based feature selection were proposed in this study. The proposed mWOA and mGWO were given a new inversed control parameter which was expected to enable more search area for the search agents in the early phase of the algorithms and resulted in a faster convergence speed. The objective of this comparative study is to investigate and compare the effectiveness of the inversed control parameter in the proposed methods against the original algorithms in terms of the number of selected features and the classification accuracy. The proposed methods were implemented in MATLAB where 12 datasets with different dimensionality from the UCI repository were used. kNN was chosen as the classifier to evaluate the classification accuracy of the selected features. Based on the experimental results, mGWO did not show significant improvements in feature reduction and maintained similar accuracy as the original GWO. On the contrary, mWOA outperformed the original WOA in terms of the two criteria mentioned even on high-dimensional datasets. Evaluating the execution time of the proposed methods, utilizing different classifiers, and hybridizing proposed methods with other metaheuristic algorithms to solve feature selection problems would be future works worth exploring.


Feature selection; metaheuristics; whale optimization algorithm; grey wolf optimizer; control parameter; high-dimensional dataset

Full Text:



O. Duncan and T. Sherer, “Feature Selection (Data Mining),†Microsoft, 2018. [Online]. Available: [Accessed: 01-May-2021].

V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, Feature Selection for High-Dimensional Data. Springer International Publishing, 2015.

A. Bommert, X. Sun, B. Bischl, J. Rahnenführer, and M. Lang, “Benchmark for filter methods for feature selection in high-dimensional classification data,†Comput. Stat. Data Anal., vol. 143, p. 106839, 2020.

B. Zhang and P. Cao, “Classification of high dimensional biomedical data based on feature selection using redundant removal,†PLoS One, vol. 14, no. 4, pp. 1–19, 2019.

A. Veeraswamy and A. M. Babu, “Classification of High Dimensional Data Using Filtration Attribute Evaluation Feature Selection Method of Data mining,†4th Int. Conf. Electr. Electron. Commun. Comput. Technol. Optim. Tech. ICEECCOT 2019, pp. 8–12, 2019.

K. S. Adewole et al., “Hybrid Feature Selection Framework For Sentiment Analysis On Large Corpora,†Jordanian J. Comput. Inf. Technol., vol. 07, no. 02, pp. 15–33, 2021.

Q. Al-Tashi, S. J. Abdulkadir, H. M. Rais, S. Mirjalili, and H. Alhussian, “Approaches to Multi-Objective Feature Selection: A Systematic Literature Review,†IEEE Access, vol. 8, pp. 125076–125096, 2020.

R. Alazaidah, M. A. Almaiah, and M. Al-Luwaici, “Associative Classification In Multi-label Classification: An Investigative Study,†Jordanian J. Comput. Inf. Technol., vol. 7, no. 2, pp. 166–179, 2021.

Y. Bouchlaghem, Y. Akhiat, and S. Amjad, “Feature Selection: A Review and Comparative Study,†E3S Web Conf., vol. 351, p. 01046, 2022.

H. Nematzadeh, R. Enayatifar, M. Mahmud, and E. Akbari, “Frequency based feature selection method using whale algorithm,†Genomics, vol. 111, no. 6, pp. 1946–1955, 2019.

H. M. Mohammed, S. U. Umar, and T. A. Rashid, “A systematic and meta-analysis survey of whale optimization algorithm,†Comput. Intell. Neurosci., vol. 2019, 2019.

W. Zhao, Z. Zhang, and L. Wang, “Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications,†Eng. Appl. Artif. Intell., vol. 87, no. September 2019, p. 103300, 2020.

A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,†Futur. Gener. Comput. Syst., vol. 97, pp. 849–872, 2019.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, “Grey Wolf Optimizer,†Adv. Eng. Softw., vol. 69, pp. 46–61, 2014.

S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,†Adv. Eng. Softw., vol. 95, pp. 51–67, 2016.

R. K. Saidala and N. Devarakonda, “Improved whale optimization algorithm case study: Clinical data of anaemic pregnant woman,†Adv. Intell. Syst. Comput., vol. 542, pp. 271–281, 2018.

X. Li and K. M. Luk, “The Grey Wolf Optimizer and Its Applications in Electromagnetics,†IEEE Trans. Antennas Propag., vol. 68, no. 3, pp. 2186–2197, 2020.

B. Sony, A. Chakravarti, and M. M. Reddy, “Traffic congestion detection using whale optimization algorithm and multi-support vector machine,†Int. J. Recent Technol. Eng., vol. 7, no. 6C2, pp. 589–593, 2019.

M. Mafarja and S. Mirjalili, “Whale optimization approaches for wrapper feature selection,†Appl. Soft Comput., vol. 62, pp. 441–453, 2018.

M. M. Mafarja and S. Mirjalili, “Hybrid Whale Optimization Algorithm with simulated annealing for feature selection,†Neurocomputing, vol. 260, pp. 302–312, 2017.

K. K. Ghosh, R. Guha, S. K. Bera, N. Kumar, and R. Sarkar, “S-shaped versus V-shaped transfer functions for binary Manta ray foraging optimization in feature selection problem,†Neural Comput. Appl., vol. 33, no. 17, pp. 11027–11041, 2021.

Q. Al-Tashi, H. Rais, and S. Jadid, “Feature selection method based on grey wolf optimization for coronary artery disease classification,†Adv. Intell. Syst. Comput., vol. 843, no. November, pp. 257–266, 2019.

P. Hu, J. S. Pan, and S. C. Chu, “Improved Binary Grey Wolf Optimizer and Its application for feature selection,†Knowledge-Based Syst., vol. 195, p. 105746, 2020.

L. Y. Yab, N. Wahid, and R. A. Hamid, A Modified Whale Optimization Algorithm as Filter-Based Feature Selection for High Dimensional Datasets, vol. 457 LNNS. Springer International Publishing, 2022.

P. Niu, S. Niu, N. liu, and L. Chang, “The defect of the Grey Wolf optimization algorithm and its verification method,†Knowledge-Based Syst., vol. 171, pp. 37–43, 2019.

E. Emary, H. M. Zawbaa, and A. E. Hassanien, “Binary grey wolf optimization approaches for feature selection,†Neurocomputing, vol. 172, pp. 371–381, 2016.

M. Zhong and W. Long, “Whale optimization algorithm with nonlinear control parameter,†MATEC Web Conf., vol. 139, pp. 1–5, 2017.

M. Abdel-Basset, G. Manogaran, D. El-Shahat, and S. Mirjalili, “A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem,†Futur. Gener. Comput. Syst., vol. 85, no. March, pp. 129–145, 2021.

F. S. Gharehchopogh and H. Gholizadeh, “A comprehensive survey: Whale Optimization Algorithm and its applications,†Swarm Evol. Comput., vol. 48, no. November 2018, pp. 1–24, 2019.

C. L. Blake and C. J. Merz, “UCI Machine Learning Repository,†1998. [Online]. Available: [Accessed: 28-Nov-2021].