Sensors (Nov 2024)

A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems

  • Jia Guo,
  • Wenhao Ye,
  • Dong Wang,
  • Zhou He,
  • Zhou Yan,
  • Mikiko Sato,
  • Yuji Sato

DOI
https://doi.org/10.3390/s24227161
Journal volume & issue
Vol. 24, no. 22
p. 7161

Abstract

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To address the limitations of traditional optimization methods in achieving high accuracy in high-dimensional problems, this paper introduces the snow leopard optimization (SLO) algorithm. SLO is a novel meta-heuristic approach inspired by the territorial behaviors of snow leopards. By emulating strategies such as territory delineation, neighborhood relocation, and dispute mechanisms, SLO achieves a balance between exploration and exploitation, to navigate vast and complex search spaces. The algorithm’s performance was evaluated using the CEC2017 benchmark and high-dimensional genetic data feature selection tasks, demonstrating SLO’s competitive advantage in solving high-dimensional optimization problems. In the CEC2017 experiments, SLO ranked first in the Friedman test, outperforming several well-known algorithms, including ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, and HHO. The effective application of SLO in high-dimensional genetic data feature selection further highlights its adaptability and practical utility, marking significant progress in the field of high-dimensional optimization and feature selection.

Keywords