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A Novel Snow Leopard Optimization for High-Dimensional Feature Selection Problems
Jia Guo1,2,3,4, Wenhao Ye5, Dong Wang6
1Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan 430205, China.
Sensors (Basel, Switzerland)
|November 27, 2024
Summary
A new Snow Leopard Optimization (SLO) algorithm balances exploration and exploitation for complex problems. SLO excels in high-dimensional optimization and feature selection, outperforming existing methods.
Area of Science:
- Computational Intelligence
- Optimization Algorithms
- Bio-inspired Computing
Background:
- Traditional optimization methods struggle with high-dimensional problems, limiting accuracy.
- Meta-heuristic algorithms offer potential but require novel approaches for complex search spaces.
Purpose of the Study:
- Introduce the Snow Leopard Optimization (SLO) algorithm, a novel meta-heuristic.
- Evaluate SLO's effectiveness in solving high-dimensional optimization and feature selection tasks.
Main Methods:
- SLO algorithm inspired by snow leopard territorial behaviors (delineation, relocation, dispute mechanisms).
- Performance evaluation using CEC2017 benchmark functions.
- Application to high-dimensional genetic data feature selection.
Main Results:
- SLO demonstrated a balance between exploration and exploitation.
- SLO ranked first in the Friedman test on CEC2017, outperforming ETBBPSO, ARBBPSO, HCOA, AVOA, WOA, SSA, and HHO.
- SLO showed practical utility in high-dimensional genetic data feature selection.
Conclusions:
- SLO is a competitive and adaptable algorithm for high-dimensional optimization.
- The study marks significant progress in high-dimensional optimization and feature selection methodologies.
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