Path Planning of Ant Colony Algorithm Based on Decision Tree in the Context of COVID-19

Abstract

Reasonable planning of travel routes can keep people away from crowded areas and reduce the probability of contracting the COVID-19. In view of the characteristics related to virus infection and human flow density, it can overcome the shortcomings of using the same pheromone initial value and slow initial convergence in route planning of ant colony optimization (ACO) algorithm. In this paper, the decision tree algorithm is used to divide the human flow density into three levels: high risk, medium risk, and low risk; and different pheromone volatility coefficients are set to change the distribution of pheromone concentration. The experimental results show that the improved ACO algorithm could help to reduce the likehood of passing through the medium-risk areas and the high-risk areas, which is reduced to less than 1%. This scheme provides an efficient route planning method for epidemic prevention and control that can be applied in the daily prevention of COVID-19 in universities.