Efficient and accurate COVID-19 detection methods are paramount in the ongoing battle against the global pandemic. In this study, we present a novel approach that leverages the Tabu Search Particle Swarm Optimization (TS-PSO) algorithm for COVID-19 detection. Unlike conventional methods, our approach combines the strengths of both global and local search strategies, enhancing the accuracy of diagnosis. We conducted extensive experiments, benchmarking our TS-PSO-based approach against established methods. The results reveal that our TS-PSO method achieves competitive performance, with improvements in sensitivity, specificity, precision, accuracy, F1 score, MCC, and FMI. This innovative approach not only advances the field of COVID-19 detection but also demonstrates its potential as a valuable tool for healthcare professionals in the ongoing fight against the pandemic.