Open Access

ARTICLE

An Adaptive and Parallel Metaheuristic Framework for Wrapper-Based Feature Selection Using Arctic Puffin Optimization

Wy-Liang Cheng¹, Wei Hong Lim^1,*, Kim Soon Chong¹, Sew Sun Tiang¹, Yit Hong Choo², El-Sayed M. El-kenawy^3,4, Amal H. Alharbi⁵, Marwa M. Eid^6,7

1 Faculty of Engineering, Technology and Built Environment, UCSI University, Cheras, 53000, Kuala Lumpur, Malaysia
2 Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, 75 Pigdons Road, Waurn Ponds, VIC 3216, Australia
3 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, Isa Town, P.O. Box 33349, Bahrain
4 Applied Science Research Center, Applied Science Private University, Amman, 11931, Jordan
5 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
6 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 11152, Egypt
7 Jadara Research Center, Jadara University, Irbid, 21110, Jordan

* Corresponding Author: Wei Hong Lim. Email:

(This article belongs to the Special Issue: Advanced Algorithms for Feature Selection in Machine Learning)

Computers, Materials & Continua 2025, 85(1), 2021-2050. https://doi.org/10.32604/cmc.2025.064243

Received 09 February 2025; Accepted 22 July 2025; Issue published 29 August 2025

Abstract

The exponential growth of data in recent years has introduced significant challenges in managing high-dimensional datasets, particularly in industrial contexts where efficient data handling and process innovation are critical. Feature selection, an essential step in data-driven process innovation, aims to identify the most relevant features to improve model interpretability, reduce complexity, and enhance predictive accuracy. To address the limitations of existing feature selection methods, this study introduces a novel wrapper-based feature selection framework leveraging the recently proposed Arctic Puffin Optimization (APO) algorithm. Specifically, we incorporate a specialized conversion mechanism to effectively adapt APO from continuous optimization to discrete, binary feature selection problems. Moreover, we introduce a fully parallelized implementation of APO in which both the search operators and fitness evaluations are executed concurrently using MATLAB’s Parallel Computing Toolbox. This parallel design significantly improves runtime efficiency and scalability, particularly for high-dimensional feature spaces. Extensive comparative experiments conducted against 14 state-of-the-art metaheuristic algorithms across 15 benchmark datasets reveal that the proposed APO-based method consistently achieves superior classification accuracy while selecting fewer features. These findings highlight the robustness and effectiveness of APO, validating its potential for advancing process innovation, economic productivity and smart city application in real-world machine learning scenarios.

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Keywords

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