Hybrid Binary Butterfly Optimization Algorithm and Simulated Annealing for Feature Selection Problem

Mohd Faizan, Fawaz Alsolami and Raees Ahmad Khan
Additional contact information
Mohd Faizan: Babasaheb Bhimrao Ambedkar University, India
Fawaz Alsolami: King Abdulaziz University, Saudi Arabia
Raees Ahmad Khan: Babasaheb Bhimrao Ambedkar University, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2022, vol. 13, issue 1, 1-18

Abstract: Feature selection is performed to eliminate irrelevant features to reduce computational overheads. Metaheuristic algorithms have become popular for the task of feature selection due to their effectiveness and flexibility. Hybridization of two or more such metaheuristics has become popular in solving optimization problems. In this paper, we propose a hybrid wrapper feature selection technique based on binary butterfly optimization algorithm (bBOA) and Simulated Annealing (SA). The SA is combined with the bBOA in a pipeline fashion such that the best solution obtained by the bBOA is passed on to the SA for further improvement. The SA solution improves the best solution obtained so far by searching in its neighborhood. Thus the SA tries to enhance the exploitation property of the bBOA. The proposed method is tested on twenty datasets from the UCI repository and the results are compared with five popular algorithms for feature selection. The results confirm the effectiveness of the hybrid approach in improving the classification accuracy and selecting the optimal feature subset.

Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 018/IJAMC.2022010104 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jamc00:v:13:y:2022:i:1:p:1-18

Access Statistics for this article

International Journal of Applied Metaheuristic Computing (IJAMC) is currently edited by Peng-Yeng Yin

More articles in International Journal of Applied Metaheuristic Computing (IJAMC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().