A Benchmarking Investigation of Evolutionary Algorithms to Resolve the COVID Sample Collection Problem

Taking a sample at home is a simple remedy for individuals in quarantine. However, with limited resources and analytical officers, laboratories are not in a position to provide timely and effective sample collection. This results in a decrease in test efficacy because the sample is not able to reach in time, resulting in a delay in appropriate medication and care. This paper aims at evaluating the efficiency and optimal use of lab’s resources in high demand of COVID test pickup from home and maps that problem as a traveling salesman problem and compares different solutions given by evolutionary algorithms. A laboratory and 19 houses are mapped in a city in India and a range matrix has been calculated using the Google Maps API. We have used Python implementation of four different evolutionary algorithms to approximate the most optimal solution. The optimization of the particle swarm exceeds the other three in terms of execution time and selection of the shortest route of 22 km. Future research could lead to better use and implementation of this algorithm, allowing labs to better serve their customers.