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Improved Whale Optimization with Local-Search Method for Feature Selection

Malek Alzaqebah1,2,*, Mutasem K. Alsmadi3, Sana Jawarneh4, Jehad Saad Alqurni5, Mohammed Tayfour3, Ibrahim Almarashdeh3, Rami Mustafa A. Mohammad6, Fahad A. Alghamdi3, Nahier Aldhafferi6, Abdullah Alqahtani6, Khalid A. Alissa7, Bashar A. Aldeeb8, Usama A. Badawi3, Maram Alwohaibi1,2, Hayat Alfagham3

1 Department of Mathematics, College of Science, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
2 Basic & Applied Scientific Research Center, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
3 Department of MIS, College of Applied Studies and Community Service, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
4 Department of Computer Science, Community College, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
5 Department of Educational Technology, College of Education, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, Saudi Arabia
6 Department of Computer Information Systems, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
7 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia
8 Deanship of Information and Communication Technology, Imam Abdulrahman bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia

* Corresponding Author: Malek Alzaqebah. Email: email

Computers, Materials & Continua 2023, 75(1), 1371-1389. https://doi.org/10.32604/cmc.2023.033509

Abstract

Various feature selection algorithms are usually employed to improve classification models’ overall performance. Optimization algorithms typically accompany such algorithms to select the optimal set of features. Among the most currently attractive trends within optimization algorithms are hybrid metaheuristics. The present paper presents two Stages of Local Search models for feature selection based on WOA (Whale Optimization Algorithm) and Great Deluge (GD). GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the search. Another version is employed using the best solution found by the WOA algorithm and exploited by the GD algorithm. In addition, disruptive selection (DS) is employed to select the solutions from the population for local search. DS is chosen to maintain the diversity of the population via enhancing low and high-quality solutions. Fifteen (15) standard benchmark datasets provided by the University of California Irvine (UCI) repository were used in evaluating the proposed approaches’ performance. Next, a comparison was made with four population-based algorithms as wrapper feature selection methods from the literature. The proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper methods. Hence, the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

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Cite This Article

M. Alzaqebah, M. K. Alsmadi, S. Jawarneh, J. S. Alqurni, M. Tayfour et al., "Improved whale optimization with local-search method for feature selection," Computers, Materials & Continua, vol. 75, no.1, pp. 1371–1389, 2023. https://doi.org/10.32604/cmc.2023.033509



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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