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A Feature Selection Method Based on an Improved Sand Cat Swarm Optimization Algorithm with Multi-Strategy Fusion.

Zhouheng Wu¹, Tao Zhou¹, Jianyong Fan²

¹College of Information Science and Technology, Shihezi University, Shihezi 832003, China.

Entropy (Basel, Switzerland)

|June 26, 2026

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This summary is machine-generated.

This study introduces an Improved Sand Cat Swarm Optimization (ISCSO) algorithm for effective feature selection in high-dimensional data. ISCSO enhances population diversity and optimization capabilities, leading to superior performance and reduced dimensionality.

Area of Science:

Computational Intelligence
Data Science
Optimization Algorithms

Background:

High-dimensional data analysis presents challenges like computational complexity and suboptimal model performance.
Existing metaheuristic feature selection methods struggle with population diversity, premature convergence, and escaping local optima.

Purpose of the Study:

To propose a novel Improved Sand Cat Swarm Optimization (ISCSO) algorithm for enhanced feature selection.
To address the limitations of existing metaheuristic approaches in complex search spaces.

Main Methods:

ISCSO incorporates a hybrid initialization using Hénon chaotic map and lens imaging reverse learning for improved population diversity.
A golden sine-based phase adjustment strategy balances global exploration and local exploitation.

Keywords:

SCSO algorithm feature selection high-dimensional data multi-strategy fusion

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A nonlinear adaptive weight mechanism and simulated annealing-based acceptance criterion enhance optimization capabilities.

Main Results:

ISCSO achieved optimal results on 82.76% of CEC2017 benchmark functions.
On UCI datasets, ISCSO attained optimal average fitness on 94.44%, significantly reducing feature dimensionality.
The algorithm consistently improved classification accuracy across tested datasets.

Conclusions:

ISCSO demonstrates superior performance compared to state-of-the-art algorithms in feature selection.
The proposed algorithm offers a competitive and reliable solution for high-dimensional data analysis and complex optimization problems.