Multi-Variable Evaluation via Position Binarization-Based Sparrow Search

The Sparrow Search Algorithm (SSA), a metaheuristic renowned for rapid convergence, good stability, and high search accuracy in continuous optimization, faces inherent limitations when applied to discrete multi-variable combinatorial optimization problems like feature selection. To enable effective multi-variable evaluation and discrete feature subset selection using SSA, a novel binary variant, Position Binarization-based Sparrow Search Algorithm (BSSA), is proposed. BSSA employs a sigmoid transformation function to convert the continuous position vectors generated by the standard SSA into binary solutions, representing feature inclusion or exclusion. Recognizing that the inherent exploitation bias of SSA and the complexity of high-dimensional feature spaces can lead to premature convergence and suboptimal solutions, we further enhance BSSA by introducing stochastic Gaussian noise (zero mean) into the sigmoid transformation. This strategic perturbation actively diversifies the search population, improves exploration capability, and bolsters the algorithm’s robustness against local optima stagnation during multi-variable evaluation. The fitness of each candidate feature subset (solution) is evaluated using the classification accuracy of a Support Vector Machine (SVM) classifier. The BSSA algorithm is compared with four high-performance optimization algorithms on 12 diverse benchmark datasets selected from the UCI repository, utilizing multiple performance metrics. Experimental results demonstrate that BSSA achieves superior performance in classification accuracy, computational efficiency, and optimal feature selection, significantly advancing multi-variable evaluation for feature selection tasks.