Intelligent planning of fire evacuation routes in buildings based on improved adaptive ant colony algorithm

Abstract

Intelligent planning of fire evacuation routes is an important guarantee for rapid emergency response. Ant colony optimization, as an intelligent bionic algorithm, has notable advantages in route planning. However, traditional ant colony optimization corresponds to a low convergence rate, is easily caught in local optimal solution, and regards route length as the only constraint. To resolve these problems, an improved adaptive ant colony optimization (IAACO) algorithm was proposed in this study. Risk, energy consumption, and route length were taken as key factors to improve the heuristic function, optimize the pheromone update function, and establish multi-objective constraints, The standards for fire evacuation better align with practical requirements. Meanwhile, the adaptive pheromone volatile coefficient was introduced to balance convergence and global searching ability. In addition, the hazard range on the grid map was visualized. The results indicate that under various complex obstacle grid maps, the path inferiority of IAACO is reduced by 61.7% and 58.4%, 43.6% and 36.7%, and 41.6% and 67.7% compared to ACO and IACO, respectively; under the condition of multiple exits, the inferiority is reduced by 63.8% and 54.6%; under the condition of multiple fire sources, the inferiority is reduced by 40.1% and 34.6%; compared with other algorithms, IAACO shows the lowest path inferiority index, 26.6. IAACO is applicable to both the dynamic planning of fire evacuation routes and the evacuation simulation software, Pathfinder, and it performs better than the built-in algorithms of Pathfinder. Facts have proved that the IAACO algorithm significantly improves the safety level of evacuation compared to traditional evacuation methods and other optimization algorithms.