Affiliations: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Correspondence:
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Corresponding author. Shaohong Feng, College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Postal code 210016, Jiangsu, China. E-mail: ytxwzzlz@163.com.
Abstract: The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning.
Keywords: Emergency evacuation, path planning, ant colony algorithm (ACO), quantum evolution theory
