  Feature selection can be defined as one of the pre-processing steps that
decrease the dimensionality of a dataset by identifying the most significant
attributes while also boosting the accuracy of classification. For solving
feature selection problems, this study presents a hybrid binary version of the
Harris Hawks Optimization algorithm (HHO) and Salp Swarm Optimization (SSA)
(HHOSSA) for Covid-19 classification. The proposed (HHOSSA) presents a strategy
for improving the basic HHO's performance using the Salp algorithm's power to
select the best fitness values. The HHOSSA was tested against two well-known
optimization algorithms, the Whale Optimization Algorithm (WOA) and the Grey
wolf optimizer (GWO), utilizing a total of 800 chest X-ray images. A total of
four performance metrics (Accuracy, Recall, Precision, F1) were employed in the
studies using three classifiers (Support vector machines (SVMs), k-Nearest
Neighbor (KNN), and Extreme Gradient Boosting (XGBoost)). The proposed
algorithm (HHOSSA) achieved 96% accuracy with the SVM classifier, and 98%
accuracy with two classifiers, XGboost and KNN.
