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

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 efficien...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing Vol. 16; no. 1; pp. 329 - 345
Main Authors: Ihsan, Muhammad, Din, Fakhrud, Zamli, Kamal Z., Ghadi, Yazeed Yasin, Alahmadi, Tahani Jaser, Innab, Nisreen
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2025
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Classification
Computational Intelligence
Crossovers
Datasets
Engineering
Exploitation
Feature selection
Heuristic
Literature reviews
Machine learning
Methods
Optimization
Optimization algorithms
Optimization techniques
Original Research
Pressure vessels
Reforming
Robotics and Automation
User Interfaces and Human Computer Interaction
Feature selection
Swarm intelligence
Grey wolf optimizer
Crossover integration
Engineering optimization
ISSN:1868-5137, 1868-5145
Online Access:Get full text
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Summary: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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ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-025-04951-x