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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Abstract

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems.

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Authors and Affiliations

  1. Faculty of IT Department of Computer Science and IT, University of Malakand, Chakdara, 18800, Khyber Pakhtunkhwa, Pakistan

    Muhammad Ihsan & Fakhrud Din

  2. Faculty of Computing College of Computing and Applied Sciences, Universiti Malaysia Pahang, Pekan, 26600, Pahang, Malaysia

    Kamal Z. Zamli

  3. Department of computer science and software engineering, Al Ain University, Abu Dhabi, 12555, United Arab Emirates

    Yazeed Yasin Ghadi

  4. Department of Information SystemsCollege of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 84428, Saudi Arabia

    Tahani Jaser Alahmadi

  5. Department of Computer Science and Information SystemsCollege of Applied Sciences, Al-Maarefa University, Riyadh, 13713, Diriyah, Saudi Arabia

    Nisreen Innab

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  1. Muhammad Ihsan
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  4. Yazeed Yasin Ghadi
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Correspondence to Muhammad Ihsan.

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Ihsan, M., Din, F., Zamli, K.Z. et al. A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection. J Ambient Intell Human Comput 16, 329–345 (2025). https://doi.org/10.1007/s12652-025-04951-x

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