Pandemic Search Algorithm: A Metaheuristic Inspiration of COVID-19 Outbreak

Abstract

Quick escalation of the Coronavirus crisis from epidemic to pandemic was unprecedented. A relatively longer asymptomatic period is a key feature of COVID-19 in rapid expansion. This paper suggests a search strategy inspired by the pandemic model of airborne disease transmission. The algorithm is based on straightforward principles globally experienced through the COVID-19 pandemic. Asymptomatic period, social distance, and reproduction numbers are fundaments of the Pandemic Search Algorithm (PSA). The performance assessment results compared to the Genetic Algorithm (GA) and Population Swarm optimization (PSO) indicate that PSA is a cost-effective method to establish a compromise between convergence rate and processing time. It can be privileged in computational problems exploring large feasible spaces due to lighter calculations, simpler structures, easier implementation, and tuning.