A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization

Liu, Ruru; Fang, Rencheng; Zeng, Tao; Fei, Hongmei; Qi, Quan; Zuo, Pengxiang; Xu, Liping; Liu, Wei

doi:10.3390/biomimetics9110701

Open AccessArticle

A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization

by

Ruru Liu

¹,

Rencheng Fang

¹,

Tao Zeng

¹,

Hongmei Fei

¹,

Quan Qi

¹,

Pengxiang Zuo

²,

Liping Xu

³ and

Wei Liu

^2,*

¹

College of Information Science and Technology, Shihezi University, Shihezi 832000, China

²

College of Medicine, Shihezi University, Shihezi 832000, China

³

College of Science, Shihezi University, Shihezi 832000, China

^*

Author to whom correspondence should be addressed.

Biomimetics 2024, 9(11), 701; https://doi.org/10.3390/biomimetics9110701

Submission received: 9 October 2024 / Revised: 12 November 2024 / Accepted: 12 November 2024 / Published: 15 November 2024

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Abstract

Feature selection (FS) constitutes a critical stage within the realms of machine learning and data mining, with the objective of eliminating irrelevant features while guaranteeing model accuracy. Nevertheless, in datasets featuring a multitude of features, choosing the optimal feature poses a significant challenge. This study presents an enhanced Sand Cat Swarm Optimization algorithm (MSCSO) to improve the feature selection process, augmenting the algorithm’s global search capacity and convergence rate via multiple innovative strategies. Specifically, this study devised logistic chaotic mapping and lens imaging reverse learning approaches for population initialization to enhance population diversity; balanced global exploration and local development capabilities through nonlinear parameter processing; and introduced a Weibull flight strategy and triangular parade strategy to optimize individual position updates. Additionally, the Gaussian–Cauchy mutation strategy was employed to improve the algorithm’s ability to overcome local optima. The experimental results demonstrate that MSCSO performs well on 65.2% of the test functions in the CEC2005 benchmark test; on the 15 datasets of UCI, MSCSO achieved the best average fitness in 93.3% of the datasets and achieved the fewest feature selections in 86.7% of the datasets while attaining the best average accuracy across 100% of the datasets, significantly outperforming other comparative algorithms.

Keywords: feature selection; global optimization; sand cat population optimization; artificial intelligence

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MDPI and ACS Style

Liu, R.; Fang, R.; Zeng, T.; Fei, H.; Qi, Q.; Zuo, P.; Xu, L.; Liu, W. A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization. Biomimetics 2024, 9, 701. https://doi.org/10.3390/biomimetics9110701

AMA Style

Liu R, Fang R, Zeng T, Fei H, Qi Q, Zuo P, Xu L, Liu W. A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization. Biomimetics. 2024; 9(11):701. https://doi.org/10.3390/biomimetics9110701

Chicago/Turabian Style

Liu, Ruru, Rencheng Fang, Tao Zeng, Hongmei Fei, Quan Qi, Pengxiang Zuo, Liping Xu, and Wei Liu. 2024. "A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization" Biomimetics 9, no. 11: 701. https://doi.org/10.3390/biomimetics9110701

APA Style

Liu, R., Fang, R., Zeng, T., Fei, H., Qi, Q., Zuo, P., Xu, L., & Liu, W. (2024). A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization. Biomimetics, 9(11), 701. https://doi.org/10.3390/biomimetics9110701

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A Novel Adaptive Sand Cat Swarm Optimization Algorithm for Feature Selection and Global Optimization

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